Tag: multidisciplinary

  • How AI is decoding the calls of the wild

    How AI is decoding the calls of the wild

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    A small pile of Bitcoin, Ethereum and other cryptocurrency coins.

    Cryptocurrency will be used to reward peer reviewers for an experimental title called ResearchHub Journal.Credit: bizoo_n/Getty

    The experimental publication ResearchHub Journal is paying peer reviewers the equivalent of US$150 per review in a specially developed cryptocurrency. The journal is hosted on ResearchHub, a platform backed by crypto entrepreneur Brian Armstrong, which aims to make science more open and efficient. “Getting paid to review is justice,” says molecular-biology consultant Pedro Paulo Gattai Gomes. But some other experts are cautious that the journal might struggle to gain a foothold in research publishing because of its unusual approach, and that the use of cryptocurrency could dissuade cautious academics.

    Nature | 5 min read

    Using tail-recognition software, researchers have tracked a humpback whale (Megaptera novaeangliae) over 13,000 kilometres — the longest migration ever seen for the species. The whale’s odyssey, from the Atlantic off Colombia to the coast of Tanzania in the Indian Ocean over the course of almost a decade, is unusual for humpbacks, who normally stick to one section of the ocean. “This could be a simple story of a deeply confused whale,” says marine biologist Alexander Werth. “But it’s more likely that this intrepid explorer is a lonely male desperately seeking mates.”

    Science | 5 min read

    Reference: Royal Society Open Science paper

    Images of a young galaxy captured by NASA’s James Webb Space Telescope have given astronomers a glimpse of what the Milky Way might have looked like when it first formed. The images capture the galaxy, dubbed Firefly Sparkle after its resemblance to the bioluminescent insects, in the process of being assembled from groups of stars around 600 million years after the beginning of the universe.

    Reuters | 5 min read

    Reference: Nature paper

    An infographic illustrating the JWST perspective and magnified view of a galaxy forming in the early Universe.

    a) Light from Firefly Sparkle and its surrounding stars was bent by the gravitational force of a cluster of other galaxies between it and the telescope, an effect called gravitational lensing. The effect of this distorted path is that the background object seems enlarged, as if it were being viewed through a cosmic magnifying glass. b) Images from the telescope captured Firefly Sparkle and two other young galaxies nearby, appropriately dubbed Firefly-Best Friend and Firefly-New Best Friend. (Nature News & Views | 7 min read, Nature paywall)

    Nature’s 10: the people who shaped science

    Portrait of Anna Abalkina standing in an atrium in a university library

    Credit: Stefanie Loos for Nature

    Research-integrity sleuth Anna Abalkina has been rooting out fraud in scientific literature for more than a decade. Her work calls out plagiarism, paper mills and hijacked journals — scam websites that clone authentic journal titles to con authors out of publication fees — with a particular focus on her homeland, Russia. This November, Abalkina flagged an unusually bold effort to clone journal sites from major publishers including Springer Nature and Elsevier, spooking the fraudsters behind the scheme into removing links to papers published in cloned journals from their website.

    Nature | 5 min read

    Read more from Nature’s 10: a series of profiles about the people behind 2024’s key scientific developments

    Features & opinion

    Artificial intelligence (AI) is helping scientists to reveal what animals are saying to each other. Researchers have already leveraged the tool to discover that both African savannah elephants (Loxodonta africana) and common marmoset monkeys (Callithrix jacchus) have names. But it’s not as simple as building Google Translate for primate chatter. Such systems need huge amounts of well-defined training data, and it’s still an open question whether animals even have ‘language’ (and what ‘language’ is). Instead, AI can do things such as help researchers comb through recordings, separate sounds and assign them to individual animals. The ultimate goal, for many, is to help protect the creatures they’re studying. “If it were possible for humans to hear from other animals in their own words, ‘Hey, stop fucking killing us’, maybe people would actually do that,” says behavioural ecologist Mickey Pardo.

    Nature | 15 min read

    This article is part of Nature Outlook: Robotics and artificial intelligence, an editorially independent supplement produced with financial support from FII Institute.

    To discover more on this topic, sign up for the free newsletter Nature Briefing: AI & Robotics.

    Satellites can be effective environmental monitoring tools, but they’re no silver bullet, warns satellite expert Lorna Finman. To accurately measure releases of the potent greenhouse gas methane, satellites should be paired with boots on the ground to verify findings and overcome issues such as weather patterns that can distort readings. “Methane monitoring is too important to leave to one tool alone,” says Finman. “Let’s make sure we get this right.”

    Nature | 5 min read

    “We cannot expect those who do not understand how we live to build tools for us,” writes artificial-intelligence researcher Nyalleng Moorosi. She argues that making AI systems that are useful for research in Africa is not simply a case of adding more data to Western-built models, which are rarely trained on African languages or culture-specific data. Instead, the research community in Africa deserves opportunities to develop its own AI systems and regulations.

    Nature | 5 min read

    QUOTE OF THE DAY

    The seabed is littered with ‘ticking time bombs’ — wrecks of ships, for example those sunk in the two World Wars, says archaeologist Fraser Sturt. Just how many wrecks there are, and whose responsibility it is to clear up the deep sea, isn’t clear, but international action and collaboration can curtail the environmental risk they pose. (The Conversation | 6 min read)

    Today, I’m invested in an epic feline love story. Two unrelated Amur tiger (Panthera tigris altaica) cubs, Boris and Svetlaya, were raised together in captivity after being found orphaned. They were released separately into the wild at 18-months old. A year later, Boris had travelled almost 200 kilometres to where Svetlaya had settled, and they later became proud parents to their own litter.

    More than a cute story, Boris and Svetlaya’s successful reintroduction into the wild has given scientists hope that the release of rescued cubs is a viable option for restoring the Amur tiger population.

    Let us know how we can get you more invested in this newsletter at [email protected].

    Thanks for reading,

    Jacob Smith, associate editor, Nature Briefing

    With contributions by Flora Graham

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  • Better data sets won’t solve the problem — we need AI for Africa to be developed in Africa

    Better data sets won’t solve the problem — we need AI for Africa to be developed in Africa

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    In May 2023 Sam Altman, chief executive of OpenAI in San Francisco, California, embarked on a 17-city world tour to promote artificial intelligence (AI) tools. One of the stops in the first week of the tour was Lagos, Nigeria. Yet, research assessing the performance of OpenAI’s chatbot ChatGPT on a data set of 670 languages shows that African languages have the least support1. The large language model GPT-4, which underlies ChatGPT, recognizes sentences written in Hausa, Nigeria’s most widely spoken language, only 10–20% of the time.

    As a computer scientist and former machine-learning engineer at Google AI in Accra, I have long known about the limitations of importing AI tools devoid of local context into Africa. And we need to give African computer scientists the opportunity to develop home-grown solutions.

    For instance, in 2018, when my colleagues and I set out to track changes in the built environment of South Africa’s historically Black townships, we quickly discovered the limitations of AI models commonly used to detect features such as houses and street patterns in aerial images. Because the post-Apartheid constitution prioritizes uplifting disenfranchised communities, our project aimed to assess whether improvements in well-being were visible in satellite imagery. Unfortunately, available AI models — trained on Western cities, which often have grid-like layouts — struggled to adapt to the nation’s unique urban landscapes.

    It took us four years to develop an AI model tailored to the local context. Meanwhile, Western researchers who had access to similar satellite and census data focused on using night-time light levels to estimate poverty rates in several African countries2 — an approach that is doomed to fail in South African townships. There, well-lit streets, a legacy of Apartheid-era discriminatory policing, could easily be misread as signalling economically prosperous urban zones.

    Our challenging experience with ‘state-of-the-art’ AI models highlights the importance of local context and lived experience. That’s why African-built small language models, such as Lesan AI — a language translation and transcription tool — can match and even outperform Western counterparts on tasks such as speech-to-text transcription. To build the model, Lesan’s co-founder, Asmelash Hadgu in Berlin, hired fluent speakers of Tigrinya and Amharic to create unique data sets and then used those to train the AI. As a technologist who speaks both languages, Hadgu was able to build a rich data set by focusing on the most descriptive parts of his language.

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  • Lipid-delivery system could treat life-threatening pregnancy complication

    Lipid-delivery system could treat life-threatening pregnancy complication

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    Nature, Published online: 11 December 2024; doi:10.1038/d41586-024-03853-w

    Pre-eclampsia is a common and dangerous pregnancy-related illness. Using tiny lipid particles to deliver messenger RNA directly to the placenta to boost blood-vessel function might prove to be an effective treatment.

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  • Formation of a low-mass galaxy from star clusters in a 600-million-year-old Universe

    Formation of a low-mass galaxy from star clusters in a 600-million-year-old Universe

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    Image preparation

    The cluster field MACS J1423.8 + 2404 was observed with JWST/NIRCam imaging using filters F090W, F115W, F150W, F200W, F277W, F356W, F410M and F444W with exposure times of 6.4 ks each, reaching a signal-to-noise ratio between 5 and 10 for an mAB = 29 point source. It was also observed with JWST/NIRISS imaging using filters F115W, F150W and F200W.

    To reduce the imaging data, we use the photometric pipeline that is presented in more detail in ref. 44. Briefly, the raw data has been reduced using the public grism redshift and line analysis software Grizli43, which masks imaging artefacts, provides astrometric calibrations based on the Gaia Data Release 3 catalogue13 and shifts images using Astrodrizzle. The photometric zero-points are applied as described in ref. 34. RGB image created using six filters of NIRCam observation of the Firefly Sparkle is shown in Fig. 1. We used images from which bright cluster galaxies and intracluster light have been removed, as described in ref. 25. The methodology for modelling and removing diffuse light from cluster galaxies and intracluster light (ICL) is presented in ref. 25. The NIRCam depths (0.3′ diameter aperture) for F090W, F115W, F150W, F200W, F277W, F356W, F410M and F444W are 7.2, 6.6, 5.2, 4.4, 3.0, 2.9, 5.5 and 4.3 nJy, respectively, and the NIRISS depths for F115WN, F150WN and F200WN are 3.6, 4.3 and 4.0 nJy, respectively41.

    Photometry of Firefly Sparkle

    We perform photometry in 10 JWST bands (NIRISS: F115WN, F150WN and F200WN; NIRCam: F115W, F150W, F200W, F277W, F356W, F410M and F444W) in which the Firefly Sparkle is detected from their morphological fit with GALFIT. In other JWST and HST filters, the Firefly Sparkle is not or barely detected; hence, we place upper limits for the entire source. As the object is resolved into at least 10 distinct clusters and a diffuse galaxy component, we perform a morphological fit using Galfit10 to extract the photometric information.

    Point spread functions are extracted empirically by median stacking bright, isolated, non-saturated stars following the methodology described in ref. 28. Convolution kernels for homogenizing all data to the F444W resolution are created with photutils.psf.matching using a SplitCosineBellWindow() windowing function to remove high-frequency noise, which results from floating-point imprecision when taking the ratio of Fourier transforms. We optimize the shape of each window function to minimize the median residual between convolved stars from each source filter that is convolved and stars from the target F444W filter.

    For the morphological fit, we create 10″ × 10″ postage stamps in all 10 filters from the BCG-subtracted images. We determine the priors for the centres of the 10 clusters by visual inspection. Although nine out of the ten appear as point sources, FF-4 has an elongated shape and appears unresolved. We first determine the central coordinates of the 10 clusters and the arc by fitting (1) an elliptical Gaussian for FF-4; (2) nine point sources for the other nine clusters; and (3) another elliptical Gaussian with the bending mode turned on for the diffuse arc to the F115W image, which has the highest resolution (smallest PSF). The free parameters are the centres and total fluxes of all the components, the radius and axis ratio of FF-4, and the radius, axis ratio and bending mode (B2) of the arc. The initial guesses for the coordinates were determined by visual inspection of the F115W image. Once we obtain the fitted central coordinates of all the components from F115W, we again fit all 11 components in F444W, which has the highest signal-to-noise ratio for the arc and FF-4, to determine the radius, axis ratio, position angles of the ellipses, and the bending mode B2 of the arc.

    We use the best-fit centre coordinates from F115W as the central coordinates in all the filters. However, instead of fixing the central coordinates, we allow GALFIT to fit for them in every filter within a very narrow range of ±0.5 pixels (0.02″) to account for the uncertainty in the PSF centre. We also fix the bending mode B2 (2.14), ellipse radius (3.9″), axis ratio (0.08) and position angle (−51.8°) of the arc from the F444W fit. We fix the morphology of FF4 also with radius = 0.59″, axis ratio = 0.1 and position angle = −53°.

    We now fit all 11 components in all 10 filters to determine their fluxes. The resulting models and residuals are shown in Extended Data Fig. 1. Residuals from the fits are negligible, as shown by χ2/ν ~ 1 in the GALFIT fits in all filters. This confirms the original visual impression that nine of the ten clusters are unresolved and an additional smooth component is present.

    To derive the uncertainty in our flux estimation, we inject the full Firefly Sparkle model in 100 random locations in our 10″ × 10″ postage stamps (avoiding the edge) and refit with the exact same setting of GALFIT. We find no significant systematic offset between the fitted flux and the injected flux for any of the 11 components, in any of the filters, showing that our photometric technique is robust to background variations across all filters. The uncertainty in the photometry is calculated from the bi-weight scale of the 100 refitted fluxes. The resulting photometry and the RGB image of the model and the residual are shown in Extended Data Fig. 1. The agreement between NIRISS and NIRCam fluxes in the three overlapping filters is another confirmation of the robustness of photometry. We have used updated zero-points34 and corrected for Milky Way extinction using the colour excess E(B − V) = 0.0272 from ref. 6 and assuming the extinction law in ref. 35 using the factor between the extinction coefficient and colour excess RV = 3.1.

    Spectroscopy extraction and spectral fitting

    NIRSpec spectroscopy has been acquired for MACS J1423.8 + 2404 and spectra were obtained for the Firefly Sparkle, FF-BF and FF-NBF. The spectra for the FF-BF were part of the sample in ref. 23, with zspec = 8.2953 ± 0.0005. The spectra were observed using the PRISM/CLEAR disperser and filter, through three Micro-Shutter Assembly (MSA) masks per cluster with a total exposure time of 2.9 ks per MSA configuration.

    The NIRSpec data were processed using the STScI JWST pipeline (software v.1.8.4 and jwst_1030.pmap) and the msaexp package31. We used the standard JWST pipeline for the level 1 processing, in which we obtained the rate fits files from the raw data. We enabled the jump step option expand_large_events to mitigate contamination by snowball residuals and used a custom persistence correction that masked out pixels that approach saturation within the following 1,200 s for any readout groups. We then used msaexp for level 2 processing, for which we performed the standard wavelength calibration, flat-fielding, path-loss correction and photometric calibration and obtained the 2D spectrum before background subtraction. As the central and upper shutters contain different clusters (see Fig. 2a to find the shutter positions), we need custom background subtraction to avoid self-subtraction. We did this by building the background 2D spectrum by stacking and smoothing the sky spectrum in the empty pixels and obtained the background subtracted 2D spectrum of Firefly Sparkle. We confirmed that this custom background subtraction method works as well as a standard drizzle background subtraction method used in the literature33, using a well-isolated galaxy spectrum from the CANUCS observation (Asada et al., in prep.). We finally extract the 1D spectrum separately in slit 1 and slit 2, by collapsing the 2D spectrum using an inverse-variance weighted kernel following the prescription in ref. 24. We verified that the uncertainty array of the 1D spectrum has the appropriate normalization by testing the distribution of spectral fluctuations in an empty sky region and finding the fractions of pixels at >1 and >2σ as expected.

    Spectral fitting in Firefly Sparkle slit 1

    The resulting 1D spectrum of Firefly Sparkle in slit 1, dominated by the cluster FF-6, is shown in Fig. 2.

    The spectrum exhibits a Balmer jump at λobs ~ 3.5 μm and a turnover at λobs 1.4 μm, probably because of two-photon emission. These features suggest that the nebular continuum should dominate over the stellar continuum in the rest frame UV to optical spectrum within slit 1 (as found for a z = 5.9 galaxy in ref. 12). We thus model the continuum of the spectrum with nebular continuum using the photoionization code CLOUDY v.23 (ref. 5). To determine the dust attenuation value in the continuum model fitting, we first measure the Hγ/Hβ ratio by fitting the Gaussian profiles. The ratio agrees well with the case B recombination, and no significant dust attenuation is indicated. Therefore, in the continuum spectral modelling, we use pure hydrogen gas irradiated by an ionizing source having black-body SED without dust attenuation. We vary the effective temperature of the black body (Teff) and the electron temperature of the (ionized) hydrogen gas (\({T}_{{\rm{e}},{{\rm{H}}}^{+}}\)), and search for the best-fitting model continuum by χ2 minimization. In the continuum fitting, we mask out emission line regions and all wavelengths λobs < 1.2 μm at which the Lyman break is seen in the slit 2 spectrum, because this region may be affected by a neutral hydrogen damping wing. The best-fit model has log(Teff/K) = 5.10 and \(\log ({T}_{{\rm{e}},{{\rm{H}}}^{+}}/K)=4.34\), which is fully consistent with the results in ref. 12. The result of continuum fitting does not change if we consider a slight dust attenuation (AV = 0.1 mag) in the fitting. As discussed in ref. 12, the effective temperature of log(Teff/K) = 5.10 is much hotter than typical massive type O stars and is suggestive of this star-forming cluster having a top-heavy IMF. The IMF of this cluster is further discussed in section ‘SED fitting analysis’.

    Note that the UV continuum turnover feature could be because of the absorption from dense neutral hydrogen either in the intergalactic medium (IGM) or in the circumgalactic medium (CGM). However, in the case of slit 1 spectrum, we expect the effect of IGM and CGM damping absorption to be negligible or limited at λobs < 1.2 μm based on the blue continuum and sharp drop-out in the slit 2 spectrum (see section ‘Spectral fitting in Firefly Sparkle slit 2’ for details of slit 2 spectrum). Considering the spatial proximity of the slit 1 and slit 2 regions (Fig. 2), we can assume the absorption feature from line-of-sight neutral hydrogen to be the same in the slit 1 and slit 2 spectra. The slit 2 spectrum is rather blue and has a sharp Lyman break starting at λobs = 1.2 μm, whereas the slit 1 spectrum shows the turnover starting at λobs ~ 1.4 μm. Thus, the turnover feature should not be because of the neutral hydrogen absorption, but rather because of the intrinsic continuum shape of the source. Nevertheless, to avoid the possible effect of the neutral hydrogen absorption, we mask out λobs < 1.2 μm in the nebular continuum fitting above (corresponding to 1,290 Å in the rest frame).

    Having the model continuum, we subtract the underlying model continuum from the observed spectrum and measure the spectroscopic redshift and emission line fluxes by fitting Gaussian profiles. The best-fitting model spectrum with nebular continuum and Gaussian profiles is shown in Fig. 2b (red solid curve). We securely detect emission lines of [O iii]λλ4959, 5007, Hβ, [Oiii]λ4363, Hγ, Hδ and [Neiii]λλ3869, 3889. We do not find significant detection of [Oii]λ3727 and obtain an upper limit for the flux of this line. There is a tentative detection of the blended line of [Oiii]λλ1661 + 1666, although the spectral resolution of the prism is low at this wavelength making this doublet difficult to securely detect and separate from Heiiλ1640. We use these emission line fluxes to estimate the physical parameters in slit 1. We first estimate the dust attenuation based on Balmer decrements. Both the Hγ/Hβ and Hδ/Hβ ratios are consistent with theoretical predictions in case B recombination21 within the uncertainties, suggesting there is no significant dust attenuation (Extended Data Fig. 3, red squares in the left). This result is consistent with the initial measurement before the continuum fitting above and supports the validity of the dust-free assumption in the nebular continuum fitting process. Therefore, we do not correct for dust attenuation in the following measurements of emission line ratios and physical parameters in this section.

    We next measure the electron temperature using temperature-sensitive emission line ratios: [Oiii]4959+5007/[Oiii]4363 and [Oiii]5007/[Oiii]1661+1666. We assume the electron density to be ne = 103 cm−3, which is consistent with recent JWST observations of similarly high-z galaxies7 and obtain consistent independent temperature measurements within the uncertainties (\({T}_{{\rm{e}},{{\rm{O}}}^{++}}={4.0}_{-0.9}^{+2.6}\,{\rm{K}}\) and \({2.9}_{-0.4}^{+0.7}\times 1{0}^{4}\,{\rm{K}}\), respectively; Extended Data Fig. 3 (right)). Note that because the [Oiii]λλ1661 + 1666 detection is tentative and potentially blended with Heiiλ1640, we consider [Oiii]λ4363 to be more reliable.

    We note that in ref. 16, the authors measured a similar ratio of [Oiii]4959+5007/[Oiii]4363 in the z = 6 galaxy RXCJ2248-ID to that of slit 1. In ref. 16, medium resolution spectroscopy was used to determine the electron density directly. They found that when using lines with higher ionization potential than O+, the electron density was higher (ne ~ 105 cm−3) than is typically found from [Oii]λ3727 (ref. 7). This high electron density leads to a lower electron temperature for their galaxy of \({T}_{{\rm{e}},{{\rm{O}}}^{++}}=2.5\times 1{0}^{4}\,{\rm{K}}\). Similarly, if we assume the electron density of ne = 105 cm−3 instead for our slit 1 spectrum, the electron temperature from [Oiii]λ4363 becomes \({T}_{{\rm{e}},{{\rm{O}}}^{++}}={3.2}_{-0.96}^{+1.6}\), which is in between the two measurements based on [Oiii]λλ1661 + 1666 and [Oiii]λ4363 when assuming ne ~ 103 cm−3 above. To consider the possibility of a somewhat higher electron density in the highly ionized region, we adopt the mean value of our two electron temperature measurements (\({T}_{{\rm{e}},{{\rm{O}}}^{++}}=3.5\times 1{0}^{4}\,{\rm{K}}\)) as our fiducial value and propagate the full range of the two measurement uncertainties into the following metallicity measurement.

    Based on the electron temperature measurement, we obtained the oxygen abundance from [Oiii]4959+5007/Hβ and [Oii]3727/Hβ ratios, following the prescription in ref. 8. We assume the electron density to be ne = 103 cm−3. The total oxygen abundance is calculated from O++/H+ and O+/H+, and the higher ionizing state oxygen is ignored30. As the [Oii]λ3727 emission line is undetected, we can obtain only an upper limit for O+/H+, but the upper limit for the abundance of the singly ionized oxygen is negligibly small as compared with the doubly ionized oxygen. We thus derived the total oxygen abundance from O++/H+, yielding \(12+\log ({\rm{O/H}})=7.0{5}_{-0.37}^{+0.22}\) (\({Z}_{{\rm{gas}}}/{Z}_{\odot }=0.0{2}_{-0.01}^{+0.04}\) assuming the solar abundance to be 8.69; ref. 38).

    We also derive the ionization parameters using the ionization-sensitive emission line ratios: [Oiii]5007/[Oii]3727 and [Neiii]3869/[Oii]3727. Following the prescription in refs. 45,46, we obtain the lower limit for the ionization parameters (log  U) from these two ratios. Both ratios provide a similar limit of log U > −2.0.

    All the emission line flux measurements and the derived physical parameters in Firefly Sparkle slit 1 are presented in Extended Data Table 1. We also compare the diagnostic emission line ratios in Firefly Sparkle with those in other galaxy population in Fig. 2d. We use the ionization-sensitive line ratio O32 ([Oiii]5007/[Oii]3727) and the temperature-sensitive line ratio RO3 ([Oiii]4959+5007/[Oiii]4363) and compare these line ratios with other [Oiii]λ4363-detected galaxies at z = 2–9 from previous JWST observations2 and those in the local universe from SDSS observations14. Extended Data Fig. 3 (middle) presents a similar comparison but uses another ionization-sensitive line ratio Ne3O2 ([Neiii]3869/[Oii]3727) instead of O32.

    Spectral fitting in Firefly Sparkle slit 2

    In contrast to slit 1, the extracted 1D spectrum in Firefly Sparkle slit 2 does not show nebular continuum features, and the blue continuum is rather smooth with a sharp drop-out because of the Lyman break at λobs ~ 1.2 μm. We thus derive the emission line fluxes from the slit 2 spectrum by fitting Gaussian profiles with the continuum being modelled by a constant offset around each emission line. We detect [Oiii]λλ4959,5007, Hβ, Hγ, Hδ, [Neiii]λ3869 and [Oii]λ3727 emission lines in the slit 2 spectrum but do not detect [Oiii]λ4363.

    We then derive the physical properties in the same way as done for Firefly Sparkle slit 1 spectrum. We measure the dust attenuation from Balmer decrement, Hγ/Hβ and Hδ/Hβ, and find both line ratios agree well with the predicted ratios under case B recombination (blue squares in Extended Data Fig. 3 (left)). This suggests that the dust attenuation is negligible in the slit 2 spectrum as well, and we do not make a dust attenuation correction.

    As we do not detect the temperature-sensitive emission lines of [Oiii]λ1666 or [Oiii]λ4363 in the slit 2 spectrum, we cannot measure the electron temperature and the metallicity from the direct-temperature method. We thus obtain only the upper limit for the electron temperature (\({T}_{{\rm{e}},{{\rm{O}}}^{++}}\)) from the non-detection of [Oiii]λ4363. The electron temperature in Firefly Sparkle slit 2 is shown to be \({T}_{{\rm{e}},{{\rm{O}}}^{++}} < 1.8\times 1{0}^{4}\,{\rm{K}}\) (1σ) or <4.5 × 104 K (3σ). To visualize the difference in physical properties in slit 1 and slit 2, we show the diagnostic emission line ratios of Firefly Sparkle slit 2 in Fig. 2d and Extended Data Fig. 3 (middle) as well.

    SED fitting analysis

    SEDs derived from our photometry were analysed using a slightly modified version of the Dense Basis method18,47 to determine non-parametric SFHs, masses and ages for our sources in Firefly Sparkle. We adopt the Calzetti attenuation law48 and a Kroupa IMF32 with a flat prior for the high-mass slope α [1., 4.]. We run fits using both the MILES stellar libraries29 and MESA Isochrones and Stellar Tracks (MIST; ref.  17), as well as the Binary Population and Spectral Synthesis (BPASS; refs. 26,36) models to consider for the presence of binary populations. As the latest BPASS version in FSPS (-bin-imf135all 100) assumes a Salpeter IMF with an upper mass cutoff of 100M and does not allow for a varying IMF, we only vary the top-heavy slope of the Kroupa IMF in the MILES + MIST runs with an upper mass cutoff of 120M. This should be considered while comparing the physical properties from the two runs, as allowing for a varying IMF based on the MILES + MIST configuration results in lower stellar masses for those runs because they are preferentially fit with top-heavy SSPs with a greater fraction of light coming from more massive stars. We fix the redshift to that found from the NIRSpec Prism spectroscopy by the [Oiii] λ4959 line at zspec = 8.296 ± 0.001. All other parameters are left free. We run the SED fits in two configurations to account for different possibilities of the nature of the individual clusters:

    1. 1.

      SSP fits: to account for the possibility that the individual clumps are star clusters, which is likely given the physical scales of the clusters and the extreme emission lines in the spectra, we modify the code to fit for instantaneous bursts of star formation, described by SSPs. In this case, we assume a flat prior in the log age of the SSP from 105 years to the age of the universe at zspec = 8.296 ± 0.001 instead of the non-parametric defaults for the SFH in Dense Basis.

    2. 2.

      Non-parametric SFH fits (Dense Basis): to fit the diffuse body of the galaxy and to account for the possibility that the clusters are nuclear star clusters or remnants of previous mergers, we also run fits with non-parametric SFHs with a Dirichlet prior. The main advantage of using Dense Basis with non-parametric SFHs is that they allow us to account for flexible stellar populations, which is important at these redshifts49 because star formation is expected to be stochastic and may be underestimated if fit using traditional parametric assumptions39,50.

    We perform our fitting in two stages—we initially perform a joint spectrophotometric fit to the NIRSpec Prism spectrum along with the HST + NIRISS + NIRCam photometry in the slits in which both exist (Extended Data Fig. 4). We correct for slit loss considering two factors—the amount of light lost due to the changing PSF as a function of wavelength and an overall multiplicative correction to match the spectrum against the photometric measurements. We modify the default Dense Basis method in this stage to additionally fit for the slope at the massive end of the IMF, the gas-phase metallicity and the ionization parameter, using the relevant parameters from FSPS (imf3, gas_logz and gas_logu). Doing so allows us to substantially constrain priors on star formation rate, IMF, dust, ionization parameter and metallicity that we then use to fit the photometry. We find that the fits are consistent with negligible dust attenuation, consistent with our estimates from measuring the Balmer decrement. We also find that our fits rule out the part of parameter space consistent with the canonical Chabrier-like or Kroupa-like IMF (with the high-mass slope ≈ 2.3) in favour of more top-heavy slopes of about \({1.5}_{-0.6}^{+0.7}\) for slit 1, which contains portions of clusters 3, 4, 5 and 6. We find weaker constraints from the spectrum for slit 2, which still skews towards top-heaviness but with large uncertainties of about \({1.7}_{-0.7}^{+0.9}\). Finally, we find estimates of both stellar and gas-phase metallicities to be sub-solar, consistent with estimates from the line ratios.

    Using our photometry (Extended Data Table 4), we now determine the stellar properties of each individual component by running a second set of fits using the same set of parameters that are used to fit the spectrophotometry. Although parameters such as the metallicity and ionization parameter are only loosely constrained by these fits, we obtain parameter estimates for the stellar masses, star formation rates and ages of the individual star clusters with uncertainties that marginalize over the variations in the other parameters.

    Both photometry and corresponding fits to the SED fit are shown in Extended Data Fig. 5, with variations in the stellar mass, age and reduced χ2 of the fits for each of the four scenarios (SSPs fits with MILES + MIST and BPASS, and Dense Basis fits with MILES + MIST and BPASS) shown in Extended Data Table 2. All 10 components have intrinsic (corrected for magnification) stellar masses of about 105–106M and sSFR of 10−7 yr−1. Although the error bars are large, the distinct colours of the clusters hint at different formation times. Although the smooth component contains a large fraction of the stellar mass, the bulk (about 57%) lies in the clusters. Extended Data Table 3 lists the physical properties of the individual components as well as the full Firefly Sparkle, BF and NBF galaxies.

    We find that the SSP fits are generally less massive compared with the Dense Basis fits, because the light from the SED is modelled by a single epoch of star formation instead of an extended episode. As light from the massive stars responsible for young star formation are much brighter than older stellar populations, they can describe the observed SED with a lower mass. However, the SSP fits often cannot capture both the UV slopes and the nebular emission in the rest-optical, as seen for clusters 1, 3, 7 and 8 in Extended Data Fig. 5 and often approximate it using a Balmer break, leading to posteriors consistent with much older ages than the median values.

    Although the tage from SSP and t50 from Dense Basis fits (Extended Data Table 2) may seem inconsistent, it is important to note that the Dense Basis fits for most star clusters indicate a sharp burst of star formation within the past 10 million years (Extended Data Fig. 6). By design, an SSP is biased towards this recent burst, whereas a non-parametric SFH can accommodate extended episodes of star formation. However, with our current data, we cannot distinguish between extended SFH in the star clusters and the contribution of light from the diffused arc.

    The masses of the clusters also scale with the top-heaviness of the high-mass end of the IMF in the MILES + MIST fits, with lower masses for more top-heavy IMF values as that scale the amount of light from massive stars. In comparison, the BPASS fits in the current setup are done at the canonical Kroupa IMF, leading to higher masses for those fits. At the same IMF slope, the masses are comparable within uncertainties for the different SPS models, and the sSFR and age/t50 values are consistent even marginalizing over the IMF posteriors. Given the observational constraints and the χ2 from the fits in Extended Data Table 2, it is not currently possible to definitively rule out any of the current fitting approaches.

    Lens modelling

    We use Lenstool9 to build a strong lensing model of the MACS 1423 cluster, to be fully presented in Desprez et al. (manuscript in preparation). This model is constrained with the three multiple image systems that were leveraged in ref. 3, for which we provide additional information obtained from the CANUCS data. The first two systems are those presented in ref. 27, one at z = 2.84 for which we account for the two clusters visible in the four images of the objects, and the second one with three images at z = 1.779 for which we identify another cluster in the two northernmost images for improved constraints. The last system is composed of five images11 for which we provide a new spectroscopic redshift measurement of z = 1.781 that is in agreement with photometric and geometric redshifts previously measured.

    The different mass components are parameterised as double Pseudo-Isothermal Elliptical (dPIE) profiles4. The model is composed of a large cluster scale mass halo, an independent galaxy scale centred on the brightest cluster galaxy and small galaxy scale mass components to account for the contribution of all cluster members that follow a mass–luminosity scaling relation22. For all galaxies, their positions, ellipticities and orientations have been fixed to these measured from the images. The final best model manages to reproduce the position of the input multiple images with a distance rms of 0.46″.

    Magnifications are obtained by generating convergence and shear maps around the Firefly Sparkle with a size of 20″ and a resolution of 10 milli-arcsec per pixel. Uncertainties in the magnifications are computed from 100 randomly selected models from the optimization of Lenstool after its convergence around the minimum χ2. The numbers provided in Extended Data Table 3 are the median and ±1σ limits on the distribution of the 100 values obtained at the position of each cluster. We measured the average magnification of the FF-arc by using the GALFIT model of the arc (in F200W) and selecting all pixels with flux >10% of maximum flux. We then computed the best magnification value for all selected pixels and computed the mean and standard deviation values for these to find the magnification of the arc (μ = 24.4 ± 6.0).

    The source plane reconstruction is made using the best GALFIT model to compute the source plane positions and magnification for the 10 star clusters. We use Lenstool to generate a source plane image reconstruction of the diffuse light of the galaxy with a smooth PSF-deconvolved model of its light profile. We use GALFIT to add 10 point sources convolved with the appropriate PSFs to the diffused source plane model at the source plane positions of the star clusters with the demagnified fluxes. This process is repeated to generate source plane models in all filters. We also generate a mass map using the same prescription, replacing the demagnified fluxes with the demagnified masses. The resulting source plane RGB image and mass map are shown in Fig. 4c,d.

    Size and surface density of star clusters

    We now investigate the spatial properties of the star clusters. Nine out of the ten star clusters are unresolved even in our highest resolution F115W NIRCam image. FF-4 has a slightly elongated shape visually but has a best-fit major axis size (0.01) smaller than the smallest PSF, making the size estimate unreliable. Hence, we use the half-width half-max of the NIRCam F115W PSF (0.02) to set an upper limit on the size of all 10 star clusters. To determine the upper limits of the sizes of unresolved sources, we use the tangential eigenvalue of magnification 1/λt, which ranges between 14 and 24. This results in a size upper limit between 4 pc and 7 pc. The central star clusters have the highest magnification and the smallest upper limits, whereas the ones near the two ends of the arc have the lowest. We use the upper limit on sizes and the demagnified stellar masses to calculate the lower limit on stellar surface densities as shown in Fig. 3b.

    Abundance matching for MW and M31 progenitors

    To estimate the range of stellar masses of progenitors of both MW-mass and M31-mass galaxies at higher redshift, we adopt a semi-empirical approach combining both simulations and observations. We assume an evolving co-moving number density with redshift, as determined by the abundance matching code from ref. 20, with z = 0 number densities of \(\log (n/{{\rm{Mpc}}}^{3})=-\,2.95\) and \(\log (n/{{\rm{Mpc}}}^{3})=-\,3.4\), respectively, for MW and M31 mass analogues. The code calculates a past median galaxy number density at z2, given an initial number density at z1, using peak halo mass functions. As the merger rate per unit halo per unit Δz is roughly constant, the evolution of the cumulative number density of progenitors of any given galaxy is a power law, with the change described by (0.16Δz) dex.

    In ref. 20, peak halo mass functions are used because the resultant median number densities are less affected by the scatter in stellar mass and luminosity. However, this scatter does affect the 1σ errors in cumulative number density. The 1σ or 68 percentile range grows with increasing redshift, but this growth is also higher for more massive galaxies.

    As the code from ref. 20 does not calculate stellar masses, we obtain the stellar mass ranges of the progenitor populations of MW and M31 analogues using stellar mass functions (SMFs) from various surveys15,19,40. We take the median cumulative number densities at each Δz to find the stellar mass associated with that number density from the corresponding SMF. Moreover, the 1σ errors on the given number density for each redshift are then used to determine the 1σ errors on the stellar mass of the progenitors. At z = 8.3, the median stellar mass of MW progenitors is \(\log ({M}_{\star }/{M}_{\odot })=6.4\pm 0.7\) and the median stellar mass of M31 progenitors is \(\log ({M}_{\star }/{M}_{\odot })=6.9\pm 0.8\). The Firefly Sparkle with a stellar mass of \(\log ({M}_{\star }/{M}_{\odot })={7.0}_{-0.3}^{+1.0}\) is definitely within 1σ stellar mass range of both Milky Way and M31 progenitors. More details on the progenitor matching technique can be found in ref. 37.

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  • Climate and health needs are driving materials advances

    Climate and health needs are driving materials advances

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    Coloured SEM showing rows of raised square pillars in a grid formation

    DNA purification microchip.Credit: Dennis Kunkel Microscopy/SPL

    Much of the scientific discourse around climate change, at least in public-policy debates, relates to tracing the status and effects of global warming, as well as solving urgent challenges such as cutting emissions. Materials science might not seem of immediate relevance here, bar the ongoing hunt for the most efficient solar energy cells. But scratch beneath the surface and it is clear that the search for materials-based solutions to environmental challenges is the driving force for many of the emerging researchers in this vast field.

    Whether it is the ongoing hunt for better battery technologies in electric vehicles, designing cooling materials that can protect buildings from heat, or ways to convert greenhouse gases into useful — and sustainable — products, materials scientists are leading the charge in finding answers to seemingly intractable problems. Successful applications of this science can drive economic growth, too, something that the countries at the heart of the green-technology revolution — many of which are in Asia — are likely to be acutely aware of. With a trade war potentially on the cards with the arrival of Donald Trump’s second presidential term in the United States, it will be interesting to see how the economic fruits of this materials science are distributed.

    Environmental concerns are far from the only motivation for materials researchers, however. Medical diagnostics is just one area in which advances in the field can hopefully usher in improvements, through the design of next-generation biosensors that allow for the real-time monitoring of patients, or even spot health issues before they arise. Provided these are used to benefit people from all walks of life, regardless of their location or income, such advances have the potential to broaden access to health care worldwide.

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  • Reversing resistance to cancer immune therapy with antibodies that target GDF-15 protein

    Reversing resistance to cancer immune therapy with antibodies that target GDF-15 protein

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    • CLINICAL BRIEFINGS

    The protein GDF-15 is a potent immunosuppressor that is overexpressed in many solid tumours and impedes the effects of cancer immunotherapy. In a first-in-human clinical trial, the GDF-15-targeting antibody visugromab reversed GDF-15-mediated resistance to immunotherapy, resulting in deep, long-lasting tumour regressions in some individuals.

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  • A lipid made by tumour cells reprograms immune cells

    A lipid made by tumour cells reprograms immune cells

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    Nature, Published online: 11 December 2024; doi:10.1038/d41586-024-03855-8

    Cancer cells often become unresponsive to multiple types of therapy. It emerges that these ‘cross-resistant’ tumour cells release lipids that reprogram cells called monocytes to stop them from activating tumour-targeting T cells.

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  • Four game-changing researchers in materials science

    Four game-changing researchers in materials science

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    Global issues, such as climate change and improving sustainability in manufacturing, and technological opportunities, including artificial intelligence and quantum computing, are driving forward the frontiers of research in materials science. These four scientists are among a new generation of researchers helping to push forward these boundaries while also bringing diverse skills to the field, ensuring a broader range of views are included in tomorrow’s solutions.

    GRACE GU: Composite creator

    Limited tone illustration of Grace Gu

    Grace Gu is using techniques such as machine learning to design composite materials.Credit: Paddy Mills

    Grace Gu is taking inspiration from a wide range of places when she comes up with designs for composite materials that are more robust, adaptable and cheaper to produce than current forms. Turning to “the hidden gems of the mathematical world” to inform her designs has been especially rewarding, says Gu, who works as a mechanical engineer at the University of California, Berkeley (UC Berkeley).

    Earlier this year, she co-authored a paper1 on composite material designs based on aperiodic monotiles — unique shapes, discovered just last year2 — that can cover a surface without ever repeating pattern. The materials were shown to be stronger, stiffer and tougher than conventional honeycomb tile structures because the non-repeating pattern creates a tightly packed network with a high tolerance of defects due to how the patterns distribute stress throughout the material. Lightweight composite materials with these characteristics are highly sought after in spacecraft and satellite manufacturing.

    “Our experiments show that the designs not only absorb energy efficiently, but also exhibit unique interlocking behaviours where the tiles actually interact with and reinforce each other,” says Gu.

    She says that she is excited by the simplicity of these designs that use a single shape because they have “immense potential for engineering applications, as they could reduce manufacturing complexity and costs”.

    “There’s actually an entire family of monotiles, which gives us a wide range of design possibilities, so far more flexibility than traditional honeycomb structures,” Gu says of the potential for these designs in creating stronger and more efficient materials.

    Gu’s ability to recognize patterns has been key to her success as a researcher. She recalls a lightbulb moment she had in 2016 when AlphaGo, an artificial-intelligence (AI) system created by London-based company Google DeepMind, defeated the world’s best player at the board game Go. Gu noticed that the grid used in Go — on which two players move stones to control territory — was similar to the pixelated 2D composite-design problem she was working on at the time.

    “We have different types of materials that can occupy different positions” on a grid, and “design strategies that are like boardgame strategies”, she says. Gu considered that if machine learning could be used to train a system to play Go — known for its immense complexity and astronomical number of possible moves — it could help her find new composite designs more efficiently.

    Gu found that AI could predict the properties of materials at a vastly accelerated pace3, and it changed how she approached this kind of work in the future. Initially applying machine learning to pixelated designs inspired by AlphaGo, Gu and colleagues have since explored graph-based and Bezier curve approaches to this work, which can capture other types of structures and geometries more effectively.

    As a woman in a male-dominated field, Gu is passionate about mentoring young women in research. She says she realized the importance of representation when, during the first class she taught at UC Berkeley, a female student raised her hand to say that she was excited for the semester because Gu was the first female professor she had been taught by at the university. “I think back at these moments and remind myself that this is the best part about being a professor; mentoring and teaching the next generation to fulfil their potential and beyond,” she says.

    Gu has received numerous accolades for her work, including a 2020 Outstanding Young Manufacturing Engineer Award from US industry body SME and The American Society of Mechanical Engineers Early Career Award in 2023. — Esme Hedley

    MARCILEIA ZANATTA: Decarbonization designer

    Limited tone illustration of Marcileia Zanatta

    Marcileia Zanatta’s research harnesses decarbonization to create sustainable materials.Credit: Paddy Mills

    Marcileia Zanatta’s desire to design new products to overcome challenges began as early as age eight, when she dreamed of inventing something that could dissolve the hair trapped in her shower drain. “I used to observe everyday problems and say: ‘When I grow up, I’m going to create something that solves this’,” she recalls.

    She went on to study industrial chemistry at university in her native Brazil and, while doing a master’s at the Federal University of Rio Grande do Sul in Porto Alegre in 2012, she began working on decarbonization — a field she finds “incredibly rewarding because of its direct impact on people’s lives”.

    More than a decade on, Zanatta, a material chemist at Jaume I University in Castelló de la Plana, Spain, is focused on finding energy-efficient ways to convert carbon dioxide into sustainable materials that can be used in chemicals, fuels, and other useful products. “This can lead to a circular economy and a net-zero future,” she says.

    In October, the province of Valencia, near where Zanatta lives, experienced one of the deadliest flooding events in Spanish history. There were also devastating floods in May across Rio Grande do Sul, the Brazilian state where she had spent a significant part of her career. “These catastrophes are painful reminders that simply reducing carbon emissions is no longer enough to address the consequences of climate change,” says Zanatta. “This reality underscores the urgency of my work.”

    She and her collaborators have invented ways to transform atmospheric carbon into compounds such as formate salts4, which can be used as de-icing agents and fluids to aid drilling, and cyclic carbonates5 — important materials in lithium batteries, cosmetics and industrial solvents.

    The latter work, she says, is one of her biggest accomplishments to date. Producing cyclic carbonates from CO2 is typically energy intensive, requiring temperatures above 100 °C, pressures 20 times greater than found in the atmosphere at sea level, and several hours to allow reactions to take place. But in 2023, Zanatta developed a more efficient method — one that takes place under mild conditions using inexpensive, commercially available organic salts such as tetrabutylammonium hydroxide. She’s even used 3D printing to create bespoke reactors that enhance reaction rates by maximizing surface area and improving the distribution of reactants around the catalyst6.

    Enabling such reactions in ambient conditions has opened up other avenues, including decarbonization methods that combine both chemical and biological reactions. “Merging the two isn’t so easy, because the second part involves microorganisms, which usually can’t withstand harsh chemical conditions,” Zanatta explains. But harnessing such biological power — some microbes can metabolize simple carbon compounds — is crucial if scientists want to produce biodegradable materials based on biopolymers from CO2. This year, Zanatta and her team successfully demonstrated how a green plastic called poly-3-hydroxybutyrate (PHB) could be produced using such a chemo-biocatalytic process, with formate salts as an intermediary — the first time PHB has been produced from captured atmospheric air4.

    Zanatta has received numerous recognitions for her work, including being named a 2023 Rising Star by the materials-science journal ACS Materials Au. But her journey hasn’t always been easy, and Zanatta says female researchers face particular challenges.

    “The years when we are close to securing a permanent position are often the same years many women are considering starting a family.” A maternity break “can completely change a woman’s career”, she says.

    Working in a male-dominated field also means that Zanatta frequently “hears sexist remarks or encounters ‘mansplaining’”, but she says it’s important to try and speak up because “engagement and awareness are key”. She offers her younger female counterparts the following advice: “Be persistent, resilient, and try not to take things personally. Always demonstrate your value, take initiative, and be confident in your leadership.” — Sandy Ong

    CONG XIAO: Quantum explorer

    Limited tone illustration of Cong Xiao

    Cong Xiao explores how quantum rules can predict the behaviour of materials.Paddy Mills

    Cong Xiao was drawn to the field of condensed-matter physics because he wanted to explore electron wavefunctions: mathematical descriptions of how electrons behave at the quantum-mechanical level.

    The fact that it can be used to develop new electronic devices shows the wonder of how “the microscopic quantum-mechanical rules can be connected to the macroscopic devices in our daily life”, says Xiao, a theoretical physicist.

    As a PhD student at Peking University in Beijing from 2018 to 2021, for instance, Xiao learnt of the “power and beauty” of the Berry phase — an important unifying theory in the field. He says it cemented his decision to pursue an area of physics that underpins important products in materials science, such as liquid crystals and silicon chips.

    Now an assistant professor at the Institute of Applied Physics and Materials Engineering at Macau University, Xiao is exploring new physical effects that can be predicted by quantum rules. For example, in sub-fields such as nonlinear transport and spintronics, he’s looking at how electrons move and interact in unusual ways. Advances in these areas could inform the design of advanced technologies such as quantum computers.

    Although his work is theoretical, Xiao says a characteristic of good research in his field is that it “not only reveals some new principle in the microscopy level but can also lead to developments in technology”.

    For example, some of Xiao’s current work in nonlinear transport has potential use in rectifiers, electrical devices that convert alternating current into direct current, a common need in communications technology. “Nonlinear transport can be used to achieve such devices, and the underlying principle is truly quantum mechanical,” says Xiao.

    An important paper7 in his career — published in 2021 — reported the first-principles calculations of the nonlinear Hall effect in antiferromagnets. The nonlinear Hall effect is the production, upon the application of an electric field, of a transverse voltage that scales nonlinearly with the applied field.

    Xiao says the paper gave other researchers the tools to perform more research on nonlinear transport in magnetic systems, as manipulation of these systems has potential applications in information technology.

    Xiao says it is sometimes difficult to decide which direction the field of condensed-matter physics is moving in. He says the biggest challenge for a theoretical researcher is “always keeping yourself always at the frontier of the research”, because ideas and topics in condensed-matter physics “move very rapidly”.

    “We have to keep learning the theoretical skills just to help us to understand the questions in broader contexts, to help to study wider physical questions. I think this is the biggest challenge, to keep exploring wider and wider research” questions.

    In the Nature Index, Xiao stands out from other early-career researchers for his relatively high materials science-related output. His Share of 3.53 for the period 2019 to 2023 places him among the leading 20 early-career researchers in the field. — Esme Hedley

    CAIO OTONI: Biomolecule magician

    Limited tone illustration of Caio Otoni

    Caio Otoni looks at new ways to upcycle biological waste into useful products.Credit: Paddy Mills

    At the State University of Campinas in São Paulo, Brazil, Caio Otoni studies the circular economy, where biological waste, such as fruit peels, coffee husks and crustacean shells, is upcycled into new products, materials and energy sources. This approach is particularly relevant to Brazil, a leading producer of sugarcane, coffee and other food crops.

    In Otoni’s lab, he and his colleagues break down waste material into its building blocks — cellulose, chitin and other polymers — and pair it with other compounds to create plastics with biodegradable and antibacterial properties.

    In a 2019 paper8, for example, Otoni and his colleagues described how they grafted cationic compounds, chemicals that contain positively charged ions, onto upcycled cellulose to create an antibacterial foam material for use in packaging, filtration and hygiene products. The foam’s positively charged compounds adhere to and disrupt the negatively charged surface of bacterial cell membranes, leading to bacterial cell death. In tests, it displayed an 85% higher antimicrobial response to Escherichia coli compared with controls.

    Otoni credits his botanist father, whose lab he would visit when he was growing up, for cultivating his appreciation for plants. But it was his time spent as an exchange student at the US Department of Agriculture’s research facility in Berkeley, California, that solidified his passion for sustainably produced materials.

    While working on a project with an Alaskan fishing company, Otoni realized how wasteful it was to throw fish skin back into the ocean. The young undergraduate devised a way to isolate collagen from the discarded skin, convert it to gelatin, and produce packaging material.

    “That was the very first project I worked on that exploited not only biorenewable resources, but also waste biomass, as a source of polymers,” says Otoni. As a PhD student and postdoc, he went on to create new materials from carrot and peach waste, as well as sugarcane bagasse — the pulpy residue left after sugarcane stalks are crushed to extract their juice.

    Securing funding as a young scientist can be tough, he concedes, “because you are competing with the big fish, the established researchers”. Working in Brazil “adds another level of complexity, because in most institutions, staff numbers are limited, meaning we have to deal with paperwork and administrative tasks in addition to our regular teaching, research and outreach duties”, says Otoni.

    “Also, in Brazil, almost everything is charged in dollars or euros, and the currency exchange makes it hard to afford some devices that are key to running competitive research.”

    It’s also been a steep learning curve to launch his own lab in 2020. “You’re trained to go to the bench and do research; you’re not trained to supervise students and manage a team. That’s something that comes with time and experience,” says Otoni, who in 2023 was the sole researcher based outside Europe and North America to win the Materials Today Rising Star Award in 2023, an annual prize given to six early-career researchers in the field of materials science and engineering.

    Otoni is keen to train other young researchers in his lab in how to upcycle waste products, as he sees it as work that can make a real impact.

    “I really believe our research on circular plastics can make a difference and help diminish the burden of plastic pollution in the world,” he says. — Sandy Ong

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  • Central control of dynamic gene circuits governs T cell rest and activation

    Central control of dynamic gene circuits governs T cell rest and activation

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    Primary human T cell isolation and expansion

    CD4+ regulatory and effector T cells were isolated from fresh peripheral blood Leukopaks (70500, STEMCELL Technologies) from healthy human donors with institutional review board-approved informed written consent (STEMCELL Technologies). The contents of the Leukopaks were washed twice with a 1X volume of EasySep buffer (DPBS, 2% FBS and 1 mM EDTA (pH 8.0)) using centrifugation. The washed cells were resuspended at 200 × 106 cells per millilitre in EasySep buffer and isolated with the EasySep Human CD4+CD127lowCD25+ Regulatory T Cell Isolation Kit (18063, STEMCELL Technologies), according to the manufacturer’s protocol. Following isolation with the kit, Treg cells were stained Alexa Fluor 647 anti-human IL-2Rα antibody (302618, BioLegend; diluted 1:25), phycoerythrin anti-human CD127 (557938, Beckon Dickinson; diluted 1:50) and Pacific Blue anti-human CD4 antibody (344620, BioLegend; diluted 1:50) and isolated with FACS performed on a BD FACS ARIA Fusion 1 (656700) to ensure a pure population without contaminating effector cells. After sorting pure CD4+CD127lowCD25+ Treg cells, the cells were seeded at 1 × 106 cells per millilitre in XVIVO-15 (02-053Q, Lonza) supplemented with 5% FCS, 55 µM 2-mercaptoethanol, 4 mM N-acetyl l-cysteine and 200 U ml−1 IL-2 (10101641, Amerisource Bergen). Teff cells were seeded at 1 × 106 cells per millilitre in RPMI-1640 supplemented with 10% FCS, 2 mM l-glutamine (25030081, Fisher Scientific), 10 mM HEPES (H0887-100ML, Sigma), 1X MEM non-essential amino acids (11140050, Fisher), 1 mM sodium pyruvate (11360070, Fisher Scientific), 100 U ml−1 penicillin–streptomycin (P4333-100ML, Sigma) and 50 U ml−1 IL-2 (10101641, Amerisource Bergen). Both cell subsets were then stimulated with ImmunoCult human CD3/CD28/CD2 T cell activator (10990, STEMCELL Technologies) at 25 µl ml−1 for Treg cells and 6.25 µl ml−1 for Teff cells. Cells were cultured at 37 °C with 5% CO2. Following activation and electroporation, cells were split 1:2 every 48 h to maintain an approximate density of 1 × 106 cells per millilitre and supplemented with respective doses of IL-2.

    Pooled CRISPR knockout screen trans-regulator editing

    Pooled screens were performed following the protocol described previously19. In brief, 24 h after stimulating and plating the T cells, the trans-regulator lentiviral library19 was added to each culture (Supplementary Table 6). The cells were counted before transduction, and virus was added at a multiplicity of infection of 0.8, using gentle mixing to disperse the viral media without disrupting cell bundling. The cells were then incubated at 37 °C for an additional 24 h, pelleted by centrifugation, and viral media were replaced with fresh media supplemented with IL-2.

    Twenty-four hours after washing, the cells were pelleted by centrifugation at 150g for 10 min, resuspended at 1.5 × 106 cells per 17.8 µl supplemented with P3 Primary Cell Nucleofector Buffer (component of V4SP-3960, Lonza) and combined with 7.2 µl ribonucleoprotein particle (RNP)/1.5 × 106 cells in a sterile 10-ml reservoir. After mixing the cells and RNPs, 25 µl of the mixture was distributed to the wells of a 96-well Nucleocuvette Plate (component of V4SP-3960, Lonza). Cells were nucleofected using code EO-115 for Treg cells and EH-115 for Teff cells on the Lonza 4D-Nucleofector System with the 96-well Shuttle. Immediately after nucleofection, 90 µl pre-warmed cell-appropriate medium was added to each well, and the cells were incubated at 37 °C for 15 min. Following incubation, cells were seeded at 1 × 106 cells per millilitre in media supplemented with IL-2.

    IL-2Rα screen sorting and library preparation

    Transduced and electroporated cells were expanded for a minimum of 6 days following editing before sorting. Cell sorting was performed 10 days following isolation for the resting screens. For the stimulated Teff screen, cells were restimulated with ImmunoCult human CD3/CD28/CD2 T cell activator (10990, STEMCELL Technologies) 9 days following initial isolation, and sorting was performed 72 h after restimulation, at the time of peak IL-2Rα expression. Before sorting, cells were counted, washed once with EasySep buffer and stained with Alexa Fluor 647 anti-human IL-2Rα antibody (302618, BioLegend; diluted 1:25). Cells were then washed and resuspended in EasySep buffer. During sorting, cells were gated on the GFP+ population (lentiviral sgRNA library marker) and the top and bottom 20% of IL-2Rα-expressing cells were sorted into 15-ml conical tubes coated with FCS. Isolated cells were pelleted, counted and lysed. Genomic DNA extraction was performed using phenol-chloroform extractions, and sgRNA libraries were amplified and prepared for sequencing using custom primers. Libraries were sequenced on an Illumina HiSeq 4000 at the UCSF CAT.

    Screen analysis

    All pooled screens were analysed with MAGeCK42 (v0.5.9.5). MAGeCK count was performed on all donors using –norm-method none followed by MAGeCK test –sort-criteria pos to identify genes that resulted in a statistically significant change in IL-2Rα expression. Results are calculated as the IL-2Rαlow bin/IL-2Rαhigh bin. Screen visualization is represented as the IL-2Rαhigh bin/IL-2Rαlow bin by flipping the sign for the fold change. All genes with an FDR-adjusted P < 0.05 were considered significant.

    Arrayed CRISPR knockout of select regulators

    Guide-loaded Cas9 RNPs were assembled with custom CRISPR RNAs (crRNAs) (Dharmacon), which were resuspended in IDT duplex buffer (11-01-03-01, IDT) at 160 µM. Sequences are provided in Supplementary Table 6. Dharmacon Edit-R CRISPR–Cas9 synthetic tracrRNA (U-002005-20, Dharmacon) also resuspended in nuclease-free duplex buffer at 160 µM was combined at a 1:1 molar ratio in a 96-well plate and incubated at 37 °C for 30 min. Single-stranded donor oligonucleotides (sequence: TTAGCTCTGTTTACGTCCCAGCGGGCATGAGAGTAACAAGAGGGTGTGGTAATATTACGGTACCGAGCACTATCGATACAATATGTGTCATACGGACACG; 100 µM stock) was added to the complex at a 1:1 molar ratio and incubated at 37 °C for 5 min. Finally, Cas9 protein (MacroLab; 40 µM stock) was added at a 1:2 molar ratio and incubated at 37 °C for 15 min. The resulting RNPs were frozen at −80 °C until the day of electroporation and were thawed to room temperature before use. Forty-eight hours following T cell activation, the cells were pelleted at 100g for 10 min and resuspended in room temperature P3 Primary Cell Nucleofector Buffer (V4XP-3032, Lonza) at 1.5 × 106 cells per 17.8 µl. Cells (1.5 × 106) were transferred to each RNP-containing well and mixed gently. Of the combined RNP cell solution, 25 µl was transferred to a 96-well electroporation cuvette plate (VVPA-1002, Lonza) and nucleofected with pulse code DS-137. Immediately following electroporation, the cells were gently resuspended in 90 µl warmed media and incubated at 37 °C for 15 min. After recovery, the cells were cultured in 96-well round-bottom plates at 1 × 106 cells per millilitre for the duration of the experiment. To prevent edge effects, the sgRNAs were randomly distributed across each plate, and the first and last columns and rows of each plate were filled with PBS to prevent evaporation. Unless otherwise specified, CRISPR–Cas9-edited cells were restimulated on day 8 following isolation for stimulation response arrayed assays with ImmunoCult human CD3/CD28/CD2 T cell activator (10990, STEMCELL Technologies).

    Genotyping of arrayed knockouts

    On the final day of the respective assay, genomic DNA was isolated using DNA QuickExtract (QE09050, Lucigen) according to the manufacturer’s protocol. Primers were designed to flank each sgRNA target site. Amplicons of the region were generated by adding 1.25 µl each of forwards and reverse primer at 10 µM to 5 µl of sample in QuickExtract, 12.5 µl of NEBNext Ultra II Q5 master mix (M0544L, NEB) and H2O to a total 25 µl reaction volume. Touchdown PCR was used with the following cycling conditions: 98 °C for 3 min, 15 cycles of 94 °C for 20 s followed by 65 °C to 57.5 °C for 20 s (0.5 °C incremental decreases per cycle) and 72 °C for 1 min, and a subsequent 20 cycles at 94 °C for 20 s, 58 °C for 20 s and 72 °C for 1 min, and a final 10-min extension at 72 °C. Amplicons were diluted 1:200 and Illumina sequencing adapters were then added in a second PCR. Indexing reactions included 1 µl of the diluted PCR1 sample, 2.5 µl of each the forwards and the reverse Illumina TruSeq indexing primers at 10 µM each, 12.5 µl of NEB Q5 master mix and H2O to a total 25 µl reaction volume. The following PCR cycling conditions were used: 98 °C for 30 s, followed by 98 °C for 10 s, 60 °C for 30 s and 72 °C for 30 s for 12 cycles, and a final extension period at 72 °C for 2 min. Samples were pooled at an equivolume ratio and SPRI purified before sequencing on an Illumina MiSeq with PE 150 reads. Analysis was performed with CRISPResso2 (v2.2.7)43 CRISPRessoBatch –skip_failed –n_processes 4 –exclude_bp_from_left 5 –exclude_bp_from_right 5 –plot_window_size 10.

    Flow cytometry analysis of arrayed knockouts

    The BioLegend FoxP3 Fix/Perm kit (421403, BioLegend) was used for staining according to the manufacturer’s protocol. Cells were washed in EasySep buffer before extracellular staining. Cells were stained with Alexa Fluor 647 anti-human IL-2Rα (CD25) antibody diluted 1:25 (302618, BioLegend), Ghost Dye Red 780 diluted 1:1,000 (13-0865-T500, Tonbo) and BV711 anti-human CD4 diluted 1:50 (344648, BioLegend) for 20 min at 4 °C and then washed once with EasySep buffer. After fixing and permeabilizing according to the kit, intracellular staining was performed with phycoerythrin anti-mouse/human Helios antibody (137216, BioLegend), KIRAVIA Blue 520 anti-human CD152 (also known as CTLA-4) antibody (349938, BioLegend) and Pacific Blue anti-human FOXP3 antibody (320116, BioLegend) each diluted 1:50 in permeabilization buffer for 30 min at room temperature. Cells were subsequently washed in permeabilization buffer and resuspended in EasySep buffer before running on the Thermo Fisher Attune NxT flow cytometer (A29004). Analysis of flow data was performed in FlowJo (v10.8.1). Gating was performed to select for lymphocytes, singlets, live cells (Ghost Dye negative) and CD4+ cells in the specified order. This population was then used to calculate the median fluorescence intensity for IL-2Rα or CTLA4. Visualization was performed in R using ggplot2 (v3.4.1).

    Cloning and lentivirus preparation

    CRISPRi sgRNAs for Perturb-seq were selected from the Dolcetto library44 and cloned into the LGR2.1 plasmid backbone (Addgene #108098). A lenti EF1a-Zim-3-dCas9-P2A-BSD was generated using Gibson assembly as previously described45. Lentivirus was prepared according to the a previous protocol25.

    Perturb-seq

    Twenty-four hours after stimulation of isolated human Treg cells and Teff cells from two donors, the cells were transduced with Zim3–dCas9 lentivirus at 3% v/v. The following day, Perturb-seq sgRNA library lentivirus was added at 0.75% v/v (multiplicity of infection of 0.3). Forty-eight hours after transduction with Zim3–dCas9, 10 mg ml−1 blasticidin (A1113903, Gibco) was added to each sample to select for dCas9+ cells. Blasticidin was replenished every 48 h until the cells were processed for sequencing. Eight days after initial isolation and stimulation of cells, half of the Treg and Teff cell culture was restimulated with ImmunoCult human CD3/CD28/CD2 T cell activator (10990, STEMCELL Technologies). On the tenth day after initial isolation, the resting and 48-h restimulated samples were collected for 10X single-cell sequencing. First, cells from each donor within the same stimulation and cell-type condition were pooled at equal concentrations. Sorting was performed to isolate live GFP+ cells from each condition. Sorted cells were processed according to the Chromium Next GEM Single Cell 5′ HT Reagent Kits v2 (Dual Index) with Feature Barcode technology for CRISPR Screening and Cell Surface Protein guide User Guide, CG000513. In brief, sorted cells were pelleted and washed once with cell staining buffer (420201, BioLegend). Next, the samples were blocked with Human TruStain FcX Fc Blocking reagent (422302, BioLegend). Meanwhile, TotalSeq-C Human Universal Cocktail V1.0 (399905, BioLegend) was prepared using cell staining buffer (420201, BioLegend), and TotalSeq-C0251 anti-human hashtag antibodies 1–4 (394661, BioLegend) were added to aliquots of the cocktail. After blocking, cells were stained with TotalSeq-C cocktail including one hashtag per cell and stimulation condition. After staining, the cells were washed three times in cell staining buffer. The samples were then resuspended in PBS with 1% BSA (Gibco) for final counting. The resulting samples were pooled across conditions and approximately 65,000 cells per well were loaded into eight wells of a Chromium Next GEM Chip N Single Cell Kit (1000375, 10X Genomics) for GEM generation. The samples were prepared for sequencing using the Chromium Next GEM (Gel Bead-in-emulsion) Single Cell 5′ HT Kit v2 (1000374), 5′ Feature Barcode Kit (1000256) and 5′ CRISPR Kit (1000451) according to the manufacturer’s protocol. GEM generation and library preparation were performed by the Gladstone Genomics Core. The resulting libraries were sequenced using a NovaSeqX Series 10B flowcell (20085595, Illumina) at the UCSF CAT.

    Perturb-seq analysis

    Fastqs for each 10X well were concatenated across lanes and flow cells. Alignment of Perturb-seq data and count aggregation for the gene expression, CRISPR sgRNA and antibody-derived tag (ADT) libraries was performed with cellranger46 count (v7.1.0) using the default settings and –expect-cells=45000 –chemistry=SC5P-R2. Gene expression fastqs were aligned to ‘refdata-gex-GRCh38- 2020-A’ human transcriptome reference acquired from 10X Genomics. SgRNA sequences were aligned to a custom reference file using the pattern TAGCTCTTAAAC(BC), whereas ADTs were aligned to the TotalSeq-C-Human-Universal-Cocktail-399905-Antibody-reference-UMI-counting.csv provided by BioLegend, also including the hashtag oligo (HTO) sequences, which were used to distinguish each cell-type and stimulation condition. Counts for each respective library were aggregated across wells with cellranger aggr using the default settings. Cells were assigned to a donor using genetic demultiplexing with Souporcell47 (https://github.com/wheaton5/souporcell). For each well, souporcell_pipeline.py was run using the bam file and cellranger count output barcodes.tsv as input in addition to the reference fasta. Donor calls shared across wells were identified using shared_samples.py using the vcf file outputs from Souporcell.

    Perturb-seq analysis was performed in R (v4.3.1) using Seurat48 (v4.3.0.1) based on code previously published49. Count matrices were imported into R using the Seurat Read10X function. After creating a Seurat object with CreateSeuratObject, quality filtering was performed to retain cells with more than 1,000 RNA features identified and less than 7.5% mitochondrial RNA. Cells without a singular donor assignment were also excluded from the object as well as cells with more than one HTO assignment as determined after running HTODemux. Low abundance transcripts were filtered using the threshold of ten cells per feature and TCR genes were removed from the primary RNA assay as they were found to be a major source of variance in the dataset. No sgRNA targets were removed as the number of cells in each condition exceeded the threshold set of 150 cells. After filtering, gene expression counts were normalized and transformed using the Seurat SCTransform function with regression of both S phase score and G2/M phase score, as described on Satija (https://satijalab.org/seurat/articles/cell_cycle_vignette.html). ADT counts were normalized using the centred log-ratio (CLR) normalization method of NormalizeData. After generating principal component analysis of both normalized and transformed RNA and ADT data, Harmony50 (v0.1.1) was used to correct for donor-associated variability in the dataset. The resulting normalized and transformed counts were used for downstream analysis unless otherwise specified. Uniform manifold approximation and projections (UMAPs) were generated using the transformed and corrected RNA and ADT counts with Seurat function FindMultiModalNeighbors followed by RunUMAP using weighted.nn. Before cell-type-specific analysis, Treg cells were manually filtered to include only cells belonging to clusters with FOXP3 and IKZF2 expression to maximize cell purity (clusters 1, 7, 8, 15, 6, 4, 19, 20, 17 and 23).

    Activation scoring was performed according to Schmidt et al.25,49. In brief, Seurat FindMarkers was used to identify differentially expressed genes between stimulated and resting non-targeting control cells within the Teff cells and Treg cells individually. Genes that had a log2-transformed fold change of more than 0.25 and were detected in 10% of restimulated or resting cells were used to generate gene weights for the score calculated as sum(GE × GW/GM), where GE is the normalized/transformed expression count of a gene, GW is the weight of the gene, and GM is the mean expression of the gene in non-target control cells of the respective cell type. Wilcoxon tests were performed to determine significance compared with non-targeting control cells with Bonferroni correction for multiple hypothesis testing (Supplementary Table 7). To observe the effect of each sgRNA within independent cell and stimulation conditions, the cells were subset by HTO. RNA and ADT normalization, transformation and donor variability correction were repeated for each subset as described above for the combined dataset. UMAPs were generated using the transformed and corrected RNA and ADT counts with Seurat function FindMultiModalNeighbors followed by RunUMAP using weighted.nn. Cell cycle quantification for each subset was performed using cycle assignments generated using the Satija cell cycle vignette referenced above.

    Pseudobulking of resting and stimulated Treg and Teff cell samples was performed using Seurat AggregateExpression grouped by HTO, target gene and donor pulling from the counts slot (sgRNAs targeting the same gene were collapsed within the same donor). Differential expression analysis was performed with the resulting pseudobulked raw counts for both RNA and ADTs. DESeq2 (v1.32.0)51 was used to identify differentially expressed genes and proteins between each sgRNA and non-targeting control sample within each cell-type and stimulation condition, using donor information as a covariate. Network plots of differentially expressed gene connections were visualized in R using influential52 (v2.2.7) and ggraph53 (v2.1.0), including only genes with an adjusted P < 0.05. Other visualization of differentially expressed genes and surface proteins was performed using ggplot2 (v3.4.1).

    Bulk RNA-seq

    At their respective timepoints, resting and 48-h restimulated cells were pelleted and resuspended at 1 × 106 cells per 300 µl of RNA lysis buffer (R1060-1-100, Zymo). Cells were pipette mixed and vortexed to lyse and frozen at −80 °C until RNA isolation was performed. RNA was isolated using the Zymo-Quick RNA micro prep kit (R1051) according to the manufacturer’s protocol with the following modifications: after thawing the samples, each sample was vortexed vigorously to ensure total lysis before loading into the extraction columns. The optional kit provided DNAse step was skipped, and instead RNA was eluted from the isolation column after the recommended washes and digested with Turbo-DNAse (AM2238, Fisher Scientific) at 37 °C for 20 min. Following digestion, RNA was purified using the RNA Clean & Concentrator-5 kit (R1016, Zymo) according to the manufacturer’s protocol. The purified RNA was submitted to the UC Davis DNA Technologies and Expression Analysis Core to generate 3′ Tag-seq libraries with unique molecular indices (UMIs). Barcoded sequencing libraries were prepared using the QuantSeq FWD kit (Lexogen) for multiplexed sequencing on a NextSeq 500 (Illumina).

    Bulk RNA-seq analysis

    RNA-seq data were processed using the pipeline previously described19. In brief, fastq adapter trimming was performed with cutadapt (v2.10). Low-quality bases were trimmed with seqtk (v0.5.0). Reads were then aligned with STAR54 (v2.7.10a) and mapped to GRCh38. UMI counting and deduplication was performed with umi_tools55 (v1.0.1) and gene counts were generated from the deduplicated reads using featureCounts (subread v2.0.1) using Gencode v41 basic transcriptome annotation. Quality control metrics were generated for each sample with Fastqc56 (v0.11.9), rseqc57 (v3.0.1) and Multiqc58 (v1.9). Differentially expressed genes between Mediator knockouts and AAVS1-knockout samples as well as stimulated and resting AAVS1-knockout samples (Supplementary Table 8) were identified from the deduplicated count matrix using DESeq2 (v1.32.0)51 in R (v4.1.0). Comparisons were made within each cell-type and stimulation condition across three donors, using donor ID as a covariate in the model. Normalized counts were generated using a DESeqDataSet containing all samples, followed by estimateSizeFactors and counts(normalized=TRUE). AAVS1-knockout normalized sample counts were then subset and averaged across donors for visualization.

    Differentially expressed genes for MED12-knockout versus AAVS1-knockout samples were defined by a cut-off of adjusted P < 0.05 (Supplementary Table 3). Comparison of the effects of MED12-knockout differentially expressed genes across stimulation-responsive categories was performed by grouping MED12-knockout versus AAVS1-knockout differentially expressed genes according to their stimulation-responsive behaviour in control cells (stimulation response = adjusted P < 0.05 and abs(log2 fold change) > 1). The Bonferroni-adjusted P value resulting from a two-tailed t-test is displayed (Fig. 4a), comparing each stimulation-responsive group to the non-stimulation-responsive group. The boxplot centre line denotes the median; the box limits indicate the upper and lower quartiles; the whiskers denote the 1.5-times interquartile range (genes per group (downregulated, not stimulation responsive and upregulated) = resting Teff cells: 272, 954 and 218; stimulated Teff cells: 242, 1,432 and 467; resting Treg cells: 269, 1,491 and 241; and stimulated Treg cells: 245, 1,945 and 426).

    A one-sided Fisher’s exact test for regulators of IL-2Rα within the differentially expressed genes downstream of MED12 was determined using screen results from the matched cell-type and stimulation conditions (Fig. 4b). Genes were subset to those targeted in the screen library and detected in CD4+ T cell bulk RNA-seq (genes per group: regulators, non-regulators = resting Teff cells: 62 and 807; stimulated Teff cells: 41 and 824; and resting Treg cells: 82 and 787). Pathway analysis was performed using PathfindR59 (v1.6.4) including KEGG, Reactome and GO-BP gene sets and the lowest P value is displayed. Visualization was performed after removing KEGG disease pathways. Apoptosis pathway visualization was performed using Cytoscape60 (v3.8.2). Gene set enrichment analysis was performed with clusterProfiler61 (v4.10.1) using msigdbr (v7.5.1) on all human gene sets.

    SEL120-34A treatment

    SEL120-34A (S8840, Selleckchem) was reconstituted in ultrapure H2O according to the manufacturer’s recommendations. Cells were treated every 48 h with a 1 µM dose, and treatment was started 48 h following cell isolation to align with the time at which cells are edited in CRISPR-based experiments. Restimulation of cells for flow cytometry and CUT&RUN was performed 10 days after initial isolation.

    Endogenous immunoprecipitation of MED12

    Immunoprecipitation base buffer (0.05 M Tris-HCl pH 7.5, 0.15 M NaCl, 0.001 M EDTA and AP MS water) was prepared the day of the experiment. Of resting and 48-hour restimulated cells, 20 × 106 cells per sample and immunoprecipitation were washed twice with PBS. Samples were then lysed in 500 µl lysis buffer per 10 × 106 cells (Base buffer, 1X PhosphoStop (04906837001, Roche), 1X Complete mini-EDTA protease inhibitor cocktail tablets (11836170001, Sigma-Aldrich), 0.50% NP-40 Surfact-Amps Detergent Solution (85124, Thermo Scientific) and incubated on nutator for 30 min at 4 °C. To digest chromatin, tip sonication was performed in round with incubation on ice between each step: 7 s 12%, 7 s 12%, 7 s 12% and 7 s 15% with four rounds of sonication total. Cell lysate was clarified by centrifugation at 3,500g for 10 min at 4 °C. A bicinchoninic acid (BCA) assay was performed for each sample, and protein concentrations were normalized across conditions. Of whole-cell lysate, 10% was reserved for input, and samples were split into MED12 (14360, Cell Signaling Technologies) immunoprecipitation and rabbit IgG isotype control (3900, Cell Signaling Technologies) immunoprecipitation conditions. In each case, 10 µg antibody was added to a 1.5 ml protein lo bind tube containing clarified protein and samples were incubated overnight at 4 °C, with rotation on a nutator. In the morning, Pierce protein A + G magnetic beads (88802, Thermo Fisher) were washed four times using 1 ml of lysis buffer per 1 ml of bead slurry, allowing the beads to bind to a magnet between each wash before removing the buffer. After the final wash, beads were resuspended in lysis buffer at the original bead slurry volume, and 50 µl was added to each sample. The lysate–antibody–bead mixture was then incubated at 4 °C for 2 h with rotation on a nutator. After incubation, beads were bound to a magnetic tube rack and washed one time with immunoprecipitation buffer + NP-40 (immunoprecipitation buffer + 0.05% NP-40) followed by three washes with a 900 µl immunoprecipitation buffer. The resulting purified proteins were processed for mass spectrometry or western blot.

    Mass spectrometry

    After immunoprecipitation, bound proteins were lysed in 8 M urea + 25 mM ammonium bicarbonate followed by reduction (5 mM dithiothreitol for 1 h at 37 °C), alkylation (10 mM iodoacetamide for 45 min at room temperature in the dark) and digestion overnight with 1 µg of trypsin (Promega). Peptide samples were applied to activated columns, and the columns were washed three times with 200 µl of 0.1% trifluoroacetic acid. Peptides were eluted with 140 µl of 50% acetonitrile and 0.1% trifluoroacetic acid and dried down by speedvac.

    Samples were resuspended in 0.1% formic acid and separated by reversed-phase chromatography using an EASY-nLC instrument (Thermo Fisher Scientific) with a 15-cm PepSep column (inner diameter of 150 µm; Bruker). Samples were acquired by data-dependent acquisition. Mobile phase A consisted of 0.1% formic acid in water, and mobile phase B consisted of 80% acetonitrile and 0.1% formic acid. Peptides were separated at a flow rate of 500 nl min−1 over the following 60 min gradient: 4–35% B in 44 min, 35–45% B in 5 min and 10 min at 88% B. Peptides were analysed by an Orbitrap Lumos MS instrument (Thermo Fisher Scientific). Data were collected in positive ion mode with MS1 resolution of 240,000, 350–1,350 m/z scan range, maximum injection time of 50 ms, radiofrequency lens of 30%. For data-dependent acquisition, MS2 fragmentation was performed on charge states 2–5 with a 20-s dynamic exclusion after a single selection and 10 ppm ± mass tolerance. All raw mass spectrometry data were searched using MaxQuant (v2.4.7) against the human proteome (UniProt canonical protein sequences, downloaded in September 2022) using default settings and with a match-between-runs enabled62.

    Mass spectrometry analysis

    Protein spectral counts as determined by MaxQuant search results were used for protein–protein interaction (PPI) confidence scoring by SAINTexpress63 (v3.6.1). Rabbit IgG pulldown samples were used as control. The total list of candidate PPIs was filtered to those that met the criteria of SAINTexpress Bayesian FDR ≤ 0.05. To quantify changes in interactions between resting and stimulated T cell states, we used a label-free quantification approach in which statistical analysis was performed using MSstats (v4.8.7)64 from the artMS (v1.18.0) R package. Visualization was performed in Cytoscape with additional connections included from the STRING database65.

    Western blots

    After affinity purification of proteins, beads were resuspended in 100 µl 2X sample buffer (4× Laemmli Sample Buffer; 1610747, Bio-Rad) with 1:10 β-mercaptoethanol (63689-25ML-F, Sigma) diluted 1:1 with 500 µl lysis buffer. Samples were boiled for 5 min at 95 °C and stored at −20 °C until further processing. Western blots were performed as previously published66. In brief, cell lysates were subjected to SDS–PAGE on 4–15% acrylamide gels and electroblotted to polyvinylidene difluoride membranes. Blocking and primary (diluted 1:1,000) and secondary antibody incubations of immunoblots were performed in Tris-buffered saline + 0.1% Tween-20 supplemented with 5% (w/v) BSA (antibodies are provided in Supplementary Table 9). Horseradish peroxidase-conjugated goat anti-rabbit and IgG (Southern Biotech) were used at a dilution of 1:30,000, and immunoreactive bands were detected using Pierce ECL Western Blotting Substrate (32106) according to the manufacturer’s instructions.

    CUT&RUN

    CUT&RUN was performed on resting and 48-h restimulated cells according to the manufacturer’s protocol with the EpiCypher CUTANA ChIC/CUT&RUN Kit and provided reagents. Samples for H3K27ac CUT&RUN were lightly crosslinked before isolation using 0.1% formaldehyde (252549, Sigma) for 1 min and quenched with 125 mM glycine (50046, Sigma). In brief, 5 × 105 T cells per reaction were washed with PBS before nuclear isolation using the EpiCypher recommended lysis buffer consisting of 20 mM HEPES pH 7.9 (Sigma-Aldrich), 10 mM KCl (Sigma-Aldrich), 0.1% Triton X-100 (Sigma-Aldrich), 20% glycerol (Sigma-Aldrich), 1 mM MnCl2 (Sigma-Aldrich), 1X cOmplete Mini-Tablet (11873580001, Roche) and 0.5 mM spermidine (Sigma-Aldrich). The cells were resuspended in 100 µl per reaction cold nuclear extraction buffer and incubated on ice for 10 min. Following lysis, nuclei were pelleted and resuspended in 100 µl per reaction of nuclear extraction buffer. The isolated nuclei were then frozen at −80 °C in extraction buffer until DNA isolation. After thawing the samples at 37 °C, the nuclei were bound to activated conA beads. After adsorption of nuclei to beads, permeabilization was performed with 0.01% digitonin-containing buffer. Antibodies for H3K27ac (13-0045, EpiCypher), H3K4me1 (13-0057, EpiCypher), H3K4me2 (13-0027, EpiCypher), H3K4me3 (13-0041, EpiCypher) and IgG (13-0042, EpiCypher) were added at 500 ng per reaction. Following overnight antibody binding, pAG-MNase addition and chromatin cleavage, 0.5 ng of the provided Escherichia coli DNA was added to each sample following chromatin cleavage by MNase. Before DNA isolation, crosslinked samples were digested overnight with proteinase K (AM2546, Invitrogen) as recommended. The provided spin columns and buffers were used for DNA isolation and purification. The resulting DNA was prepared for sequencing using the CUTANA CUT&RUN Library Prep Kit (14-1002) according to the manufacturer’s protocol.

    CUT&RUN analysis

    Pooled libraries were sequenced on a NextSeq 500 (H3K27ac) and NextSeq 2000 with 2 × 75 or 2 × 50 paired-end reads, respectively. Bcl2fastq (v2.19) with the settings –minimum-trimmed-read-length 8 was used to generate fastqs. CUT&RUN data analysis was performed according Zheng et al. with the recommended settings unless otherwise specified below67. In brief, the fastqs were trimmed with cutadapt (v1.18). Bowtie2 (v2.2.5)68 was used to align the trimmed fastqs to GRCh38 using settings –local –very-sensitive –no-mixed –no-discordant –phred33 –dovetail -I 10 -X 700 -p 8 -q and E. coli (EMBL accession U00096.2) with settings –local –very-sensitive –no-overlap –no-dovetail –no-mixed –no-discordant –phred33 -I 10 -X 700 -p 8 -q. Bam files were generated with SAMtools69,70 (v1.9) view -bS -F 0 × 04 and bam-to-bed conversion performed with bedtools (v2.30.0) bamtobed -bedpe. Bedfiles were filtered to include only paired reads of less than 1,000 bp with the command awk ‘$1==$4 & & $6-$2 < 1000 {print $0}’ samplename.bed before generating bedgraph files using bedtools (v2.30.0) genomecov -bg. Peak calling was performed using the bedgraph files as input with SEACR71 (v1.3). Each target bedgraph file was compared to the respective donor and knockout condition IgG file to identify peaks above the background using the norm and stringent options for H3K27ac samples. Spike-in scaling was performed before methylation peak calling with SEACR using the IgG file as background without normalization (non option) and with the stringent option.

    Before generating a peak by sample matrix for each target, ChIP–seq blacklist regions were removed from the data. The sample matrix was reduced across all peaks within the dataset, and H3K27ac peaks were segmented into regions of 5,000 bp maximum length. Regions of differential acetylation or methylation between the regulator knockouts and AAVS1-knockout samples were identified for the peaks called across any of the samples from bam files using DESeq2 (v1.32.0)51 in R (v4.1.0; Supplementary Table 10). Comparisons were made within each cell-type and stimulation condition using AAVS1s prepared in the same batch of samples. Gene annotation was performed using the gene with the nearest TSS to each region with the GenomicRanges72 (v1.44.0) nearest function. Final bedgraph scaling was performed based on peak coverage across all samples and conditions using DESeq2 (v1.32.0) sizefactors. SEL120-34A and H2O treatment samples were compared as described for MED12-knockout and AAVS1-knockout samples, using the peak matrix from MED12-knockout and AAVS1-knockout samples to maximize detection of overlapping regions across datasets.

    ChIP–seq

    A portion of edited Teff cells were restimulated with ImmunoCult human CD3/CD28/CD2 T cell activator (10990, STEMCELL Technologies) 10 days following isolation and collected 48 h later. Up to 1–2 × 106 Teff cells were crosslinked in PBS with 1% methanol-free formaldehyde (28908, Thermo) for 10 min at 18–22 °C followed by quenching in glycine at 125 mM final concentration. Crosslinked cell pellets were snap-frozen in liquid nitrogen and stored at –80 °C. Nuclei were isolated from thawed, crosslinked cells via sequential lysis in LB1 (50 mM HEPES-KOH pH 7.5, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% IGEPAL CA-360 and 0.25% Triton X-100), LB2 (10 mM Tris-HCl pH 8, 200 mM NaCl, 1 mM EDTA and 0.5 mM EGTA) and LB3 (10 mM Tris-HCl pH 8, 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 1% sodium deoxycholate (NaDOC) and 0.5% N-laurylsarcosine) supplemented with 0.5 mM phenylmethylsulfonyl fluoride (PMSF; P7626, Sigma) and 0.5X protease inhibitor cocktail (PIC; P8340, Sigma). Chromatin was sheared on a Covaris E220-focused ultrasonicator using 1-ml milliTubes (520128, Covaris) with 140 W peak incident power, 5% duty factor, 200 cycles per burst, 6 °C temperature setpoint (minimum of 3 °C and maximum of 9 °C), fill level 10, and time 12–14 min to obtain a target size of 200–700 bp. Formaldehyde crosslinked, sheared mouse CD8+ T cell chromatin was spiked in at 2.5% of human Teff chromatin based on fluorometric (Qubit, Q33238, Thermo) or OD260 (Nanodrop, 912A1099, Thermo) quantification. Triton X-100 was added to a final concentration of 1% before immunoprecipitation for 16 h at 4 °C with 2–8 µg of indicated antibodies (Supplementary Table 9) bound to a 1:1 mixture of protein A and protein G magnetic beads (10001D and 10003D, Thermo). Bead-bound antibody–chromatin complexes were sequentially washed three times with wash buffer 1 (20 mM Tris pH 8, 150 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 1% Triton X-100, 0.1% SDS and 0.1% NaDOC), twice with wash buffer 2 (20 mM Tris-HCl pH 8, 500 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 1% Triton X-100, 0.1% SDS and 0.1% NaDOC), twice with wash buffer 3 (20 mM Tris-HCl pH 8, 250 mM LiCl, 1 mM EDTA, 0.5% IGEPAL CA-360 and 0.5% NaDOC), twice with TET (10 mM Tris-HCl pH 8, 1 mM EDTA and 0.2% Tween-20) and once with TE0.1 (10 mM Tris-HCl pH 8, 0.1 mM EDTA, 0.5 mM PMSF and 0.5X PIC) supplemented with 0.5 mM PMSF and 0.5X PIC. Beads were resuspended in TT (10 mM Tris-HCl pH 8 and 0.05% Tween-20) before on-bead library preparation using the NEBNext Ultra II DNA Library Prep Kit (E7370L, NEB) as previously described73. ChIP–seq libraries were multiplexed for paired-end (2 × 50 bp) sequencing on an Illumina NextSeq 2000 instrument.

    ChIP–seq analysis

    Reads were trimmed to remove adapters and low-quality sequences and aligned to the hg38 and mm10 reference genome assemblies with bwa74 (v0.7.17-r1188) before filtering to remove duplicates and low-quality alignments including problematic genomic regions75 using the nf-core/ChIP–seq pipeline76 (v2.0.0; https://doi.org/10.5281/zenodo.3240506) with default parameters. Normalization to mouse spike-in chromatin was performed by scaling counts to the quotient of the ratios of human:mouse ChIP reads and human:mouse input reads as previously described77. CXXC1 peaks for visualization were identified using bam files from all AAVS1-knockout donors for MACS2 (v2.2.6)78 callpeak -q 0.05 with input samples used to define the background. High-confidence MED12 peaks were identified using bam files from all AAVS1-knockout donors for MACS2 callpeak -q 0.05 with MED12-knockout samples used to define the background (Supplementary Table 11). Utilization of high-confidence peaks generated from knockout controls reduced potential false-positive signals from the ChIP samples, providing a more rigorous assessment of MED12 binding79,80. ChIP–seq blacklist regions were removed from CXXC1 and MED12 peaks before analysis.

    Polymerase pausing analysis

    The polymerase pausing index was calculated as previously described33 as (TSS coverage/TSS length)/(gene body coverage/gene body length). Gencode v43 gene structures were selected for APRIS genes and filtered to include only genes expressed in Teff bulk RNA-seq data (defined from AAVS1 Teff RNA-seq base mean > 10). The TSS region of each gene was defined as 200 bp upstream and downstream of the TSS. The gene body was defined as the region 400 bp downstream from the TSS plus 400 bp past the final exon of the gene. Rtracklayer81 (v1.62.0) was used to import spike-in scaled RNA Pol II CTD bigwigs, and GenomicAlignments (v1.38.2) summarizeOverlaps() was used to determine the coverage within the defined gene regions.

    CUT&RUN and ChIP–seq visualization

    Visualization of scaled tracks was performed with rtracklayer (v1.62.0) and ggplot2 (v3.5.1) with smoothing. APRIS gene structure was used for gene annotation with gggenes (v0.5.0). CD4+ Treg STAT5A ChIP–seq data were accessed from ChIP Atlas82, SRX212432 and GSM1056923, and generated by Hoffmann et al.31. Deeptools (v3.5.5)83 was used to generate profile plots of ChIP–seq data using computeMatrix scale-regions -b 3000 –regionBodyLength 5000 -a 3000 –skipZeros with scaled bigwigs, and a bed file of all expressed genes (defined from AAVS1 Teff RNA-seq base mean > 10) as input, followed by plotProfile –perGroup.

    MED12 CAR activation scoring

    MED12 CAR RNA-seq data from Freitas et al. was accessed from the Gene Expression Omnibus, using the downloader to retrieve the raw counts file (GSE174279_raw_counts_GRCh38.p13_NCBI.tsv.gz). First, DESeq2 (v1.32.0) was used to identify differentially expressed genes between AAVS1-knockout stimulated and resting samples. The top upregulated genes were defined using the following criteria: adjusted P < 0.01, log2 fold change > 2 and base mean > 10. The resulting 797 genes were used to generate a gene signature of activation. Normalized counts for the MED12-knockout and AAVS1-knockout resting and stimulated samples were generated with DESeq2 vst and converted to a summarized experiment with SummarizedExperiment84 (v1.22.0). The normalized count matrix and activation score were used as input for GSVA85 (v1.40.1) using the gsva function with min.sz=10, max.sz=6000, kcdf = ‘Poisson’. Visualization of the resulting gene scores was performed with ggplot2(v3.4.1) and adjusted P values were generated using rstatix (v0.7.2).

    Activation-induced cell death assays

    Activation-induced cell death assays were performed using titrated amounts of ImmunoCult human CD3/CD28/CD2 T cell activator (10990, STEMCELL Technologies) in addition to 50 U ml−1 of IL-2. Active caspase-3/7 staining was performed 72 h following addition of stimulus using the CellEvent Caspase-3/7 Green Flow Cytometry Assay Kit (C10427, Invitrogen) according to the manufacturer’s protocol. Gating of the apoptotic population was performed on the lymphocyte gate and defined as active caspase-3/7 positive and SYTOX nucleic acid stain negative. FAS staining was performed using phycoerythrin anti-human CD95 (Fas) antibody (305608, BioLegend; diluted 1:50).

    Luminex assays

    On day 12 following isolation for Teff cells and day 8 following isolation for Treg cells, cells were plated in 96-well plates in cytokine-free medium at a density of 2 × 105 cells per well. Cells were restimulated with ImmunoCult human CD3/CD28/CD2 T cell activator (10990, STEMCELLl Technologies) and supernatant was collected after 24 h. The supernatant was stored at −80 °C until processing by EVE Technologies with the Luminex xMAP technology on the Luminex 200 system. After a serial titration to determine appropriate dilutions, samples were run in technical duplicate, and Luminex 48 plex human panel A was run for Teff cells (diluted 1:20) and Treg cells (diluted 1:5). The multi-species TGF 3 plex panel was also run for Treg cells (undiluted). Technical replicates were averaged by EVE for each sgRNA and donor combination to determine protein concentration. Cytokines with more than one sample out of range were removed from the analysis to exclude low abundance proteins (Supplementary Table 12).

    Suppression assays

    Donor-matched Teff cells were isolated and frozen at −80 °C without activation until 24 h before the assay. Teff cells were thawed and cultured overnight at 2 × 106 cells per millilitre with 10 U ml−1 IL-2. On the day of the assay, Teff cells were counted and stained with CellTrace Violet (C34557, Invitrogen) according to the manufacturer’s protocol using a 1:2,000 dilution of dye. Assay plates were assembled with 1 × 105 Teff cells per well in 96-well round bottom plates with titrated amounts of Treg cells ranging from 1:1 to 8:1 Teff cells:Treg cells. One well per condition was also included of 1 × 105 Treg cells and 5 × 104 Teff cells (1:2 Teff cells:Treg cells), as well as resting and stimulated Treg cells and Teff cells individually as controls. Treg Suppression Inspector (130-092-909, Miltenyi Biotec) iMACS particles were prepared and added to the appropriate wells according to the manufacturer’s recommendations. Assays were performed in technical triplicate for four donors, and plates were incubated for 96 h at 37 °C. At the time of readout, cells were stained with Alexa Fluor 647 anti-human IL-2Rα (302618, BioLegend), BV711 anti-human CD4 (344648, BioLegend) and Ghost Dye Red 780 (13-0865-T500, Tonbo), and analysed on the Attune NxT flow cytometer (A29004).

    Analysis of flow data was performed in FlowJo (v10.8.1) with gating to select for lymphocytes, singlets, live cells (Ghost Dye negative), CD4+ T cells and Teff cells (CellTrace Violet+CD25low). A gate was then set for each donor using the non-stimulated Teff-only control (CellTrace Violet high peak) to establish a proliferative Teff count. A gate was also set for iMACS beads by selecting non-lymphocytes, beads using forward scatter area (FSC-A) and Ghost Dye. An absolute proliferating Teff cell count was then established using the formula (proliferative Teff cell count × input bead count)/(beads), which adjusts for variations in stimulation and collection abnormalities. Percentage suppression was calculated as (100 – (absolute proliferating Teff cell count/absolute proliferating Teff cell count of stimulated responder only condition)) × 100. The median of the technical replicate collection plates was used to calculate percent suppression and absolute proliferating Teff cell count per donor for visualization.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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