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Lopez, R.

Publications and source records attributed to Lopez, R..

At least 19 recordsLinked to original sources

Archetype analysis of multiplexed antibody imaging reveals interpretable morphological programs in optical pooled screens

High-content imaging-based screens with small molecule or genetic perturbations rely on rich optical phenotypes, yet how distinct subcellular markers contribute complementary biological information remains unclear. Here, we introduce CellMAPP, a multiplexed organelle imaging platform that uses optical pooled screens with iterative antibody staining to map cellular architecture under perturbation. Using 17 markers across 524 genetic perturbations in MCF-7 cells, we systematically examined how individual, and combinations of, markers capture diverse morphological responses. We find that markers provide complementary views of perturbation effects, with increased multiplexing improving signal and the breadth of hit recovery, although some phenotypes are better recovered from a subset of features rather than a full profile. Because non-molecular image-based profiles can be challenging to link with interpretable biology, we developed CHART (available at https://github.com/Genentech/CHART), an archetype analysis framework that identifies distinct reference morphological states for each marker and uses multimodal large language models (MLLMs) to automatically annotate them, connecting cellular images to recognizable organelle phenotypes. Integrating archetypes across multiple markers revealed modular morphological programs that describe coordinated organelle reorganization in response to perturbation. This framework identified regulators of defined organelle states such as micronuclei, nominated novel gene functions and revealed complex patterns of multi-organelle reposition. Together, optical profiling with MAPP and CHART provides an interpretable, scalable framework for image-based screens that sheds light on rules governing cellular structural reprogramming.

systems biology↗

Fragmentation Patterns of Human Telomeric Chromatin in Plasma cfDNA

The unique chromatin structure at telomeres protects the linear ends of chromosomes from DNA surveillance machineries. Here, we demonstrate that circulating cell-free DNA (cfDNA) from plasma can map chromatin structure at telomeres. We find that the telomeric 6-mer repeats (TTAGGG/CCCTAA) are the most abundant circulating 6-mers in cfDNA. Telomeric sequences in cfDNA contain subnucleosomal footprints distinct from the rest of the genome, arising from specific cleavages in the C-rich strand, and a nucleosome repeat length of 145 bp, markedly shorter than the [~]170 bp observed genome-wide. The abundance of cfDNA telomeric footprints decreases with age, and this decline is exacerbated by Dyskeratosis Congenita (DC), a telomere biology disorder. Promoter subnucleosome enrichment from cfDNA identifies DC-specific gene signatures that reflect disease states and show enrichment towards chromosome ends. In this work, we demonstrate that cfDNA captures telomere chromatin structure and its genome-wide impact non-invasively, including disease-specific signatures in DC.

genomics↗

Scalable multimodal mapping of macrophage regulatory architecture by integrating optical and transcriptomic pooled screens

Understanding how genetic perturbations reshape cellular states requires measuring diverse phenotypic modalities at scale. Here, we present PerturbPair, a platform that combines parallel Perturb-Seq and optical pooled screening (OPS) in primary mouse bone marrow-derived macrophages stimulated with lipopolysaccharide. Profiling over 334,000 single-cell transcriptomes across [~]1,000 gene perturbations and 7.8 million imaging-phenotyped cells across [~]3,000 gene perturbations, we reveal high concordance between transcriptomic and optical perturbation signatures. While RNA and imaging phenotypes were broadly concordant, OPS demonstrated superior sensitivity for weak-effect perturbations owing to greater cell throughput, and captured post-transcriptional regulatory events--such as protein trapping and mTOR-dependent phosphorylation--that left no detectable transcriptional footprint. To exploit cross-modal relationships, we developed EB-MoCAVI, an empirical Bayesian variational inference framework that both imputes RNA profiles for perturbations measured only by imaging--effectively tripling our Perturb-Seq dataset in silico--and denoises transcriptomic estimates for perturbations with sparse cell coverage by leveraging matched imaging data as a regularizer. A secondary screen validated the imputed transcriptional profiles and corroborated the cytosolic iron-sulfur assembly pathway as a candidate restraint on macrophage interferon tone. Integrating measured and imputed profiles with rare-variant burden statistics from the UK Biobank identified disease-specific macrophage gene programs and causal regulatory nodes for monocyte counts, type 2 diabetes, and inflammatory bowel disease. PerturbPair establishes a generalizable framework for multimodal perturbation atlases, pointing toward quantitative, causally-informed cross-modal models of cellular behavior.

genomics↗

Massively multiplex multimodal chemical screens at single-cell resolution

Recent applications of scRNA-seq for massively multiplexed chemical screens have enabled comprehensive profiling of drug responses at unprecedented scale and resolution. However, current assays remain limited to RNA readouts, lacking information on other phenotypic and mechanistic layers such as chromatin accessibility, protein abundance and post-translational modifications. Here, we introduce a scalable framework for multimodal chemical screens, combining parallel small-molecule perturbations with multimodal readouts. We extend existing experimental platforms into icCITE-plex and DOGMA-plex, enabling joint profiling of RNA, protein, and epigenomic responses to thousands of chemical perturbations in parallel. To systematically decode the regulatory circuitry underlying these responses, we develop MoCAVI, a contrastive analysis framework that disentangles the effect of small molecules from control variation in multimodal measurements, and PERCISTRA, a pipeline that infers causal links between chromatin accessibility and gene expression. Applied across ~410,000 primary T cells under ~2,800 conditions, our approach resolves compound-specific mechanisms, highlights off-target effects, and links chromatin accessibility changes to transcription factor networks in primary T cells. Our results establish a generalizable platform for profiling and analyzing cellular responses to chemical perturbations across multiple modalities.

genomics↗

Effects of protein interface mutations on protein quality and affinity

Accurately modeling antibody-antigen interactions requires distinguishing intrinsic binding affinity ("protein-interaction") from protein biophysical properties ("protein-quality"), including folding, stability, and expression. However, high-throughput mutational measurements commonly used to train and benchmark computational models often conflate these effects, obscuring the true determinants of molecular recognition. Here, we present an experimental and analytical framework to disentangle protein-interaction effects from protein-quality effects in single-domain antibody (VHH)-antigen binding. Using a large-scale deep mutational scanning (DMS) dataset spanning four VHH-antigen complexes, with single and double mutations in both partners, we introduce control binders to quantify protein-quality changes independently of protein-interaction. This enables decomposition of experimentally measured affinity into protein-interaction and protein-quality components at scale. Leveraging the disentangled dataset, we evaluated state-of-the-art structure- and sequence-based models for protein-quality and protein-interaction prediction and show that their performance largely reflects protein-quality rather than protein-interaction effects. Our results highlight a major confounder in current datasets and suggest that accounting for protein-quality will be essential for training next-generation affinity-prediction models. Nomenclature Antibody related termsO_LIPrimary VHH: The VHH of a VHH-antigen complex for which the paratope and the epitope weremutated. C_LIO_LIControl VHH: A second VHH that binds to the same antigen as the primary VHH but has non-overlapping epitope positions and therefore does not bind to any of the mutated antigen positions. C_LI Affinity-related termsO_LIReal Affinity: "The strength of the interaction between two [...] molecules that bind reversibly (interact)" 1. In the context of antibody-antigen binding, it quantifies interactions between active proteins (which are expressed and correctly folded 2 and are therefore functionally and biologically active (see below). It is commonly quantified by the equilibrium dissociation constant, KD. C_LIO_LIObserved affinity ({degrees}KD): The interaction strength experimentally measured between two molecules. Unlike real affinity, this value is confounded by the biophysical properties of the individual binding partners, specifically their folding, stability, and expression levels. Consequently, the observed affinity often differs from the real/intrinsic affinity if a significant fraction of the protein population is inactive 3. NOTE: Unless otherwise specified, {degrees}KD is reported in - log10 space. For example, a {degrees}KD of -9 corresponds to 10-9M or 1nM. C_LIO_LIChange in observed affinity ({Delta}{degrees}KD): The shift in the observed affinity between two proteins upon mutation, reported as the log10-transformed fold change. A value of 1 reflects a 10-fold difference, a value of 2 a 100-fold difference, etc. This aggregate change resolves into two distinct biophysical components 2, 4: O_LIProtein-interaction change: The change in the intrinsic thermodynamic affinity between the two binding partners, each in its active state (i.e., the specific change in interface Gibbs free energy because both enthalpy and entropy are considered). C_LIO_LIProtein-quality change: The change in the fraction of the mutated protein population that is biologically active - meaning it is expressed, correctly folded, and stable 2, 5. O_LIFolding: The process that guides the polypeptide chain toward its native conformation, which is a prerequisite for forming a functional binding site. C_LIO_LIStability: The thermodynamic capacity to maintain the folded structure over time and under physiological conditions. Stability (decrease in Gibbs free energy from the unfolded to the folded state) ensures the binding interface remains intact and prevents competing processes such as aggregation 6. C_LIO_LIExpression: The steady-state abundance of the protein. This is largely dependent on proper folding and stability, as cellular quality control mechanisms degrade proteins that fail to fold or remain stable at functional concentrations. C_LI C_LI C_LIO_LIChange in relative affinity ({Delta}{Delta}{degrees}KD): the difference between the {Delta}{degrees}KD of the primary VHH compared to the control VHH for a given epitope mutation. C_LI Model-related termsO_LIESM-IF1 sc: Single-chain (sc) structure-conditioned inverse folding model (ESM-IF1), using the isolated monomer structure of the mutated protein: either the VHH or the antigen 7. C_LIO_LIESM-IF1 mc: Multi-chain (mc) structure-conditioned model (ESM-IF1), using the full complex structure (both antibody and antigen) 7. C_LIO_LIStability prediction score: Score that represents the predicted change in stability based on a single mutation, normally represented as {Delta}{Delta}G. C_LI

molecular biology↗

The Nuclear-Cytoskeletal Interface Is a Vulnerability in Aging Endothelium

Vascular aging is a fundamental driver of age-related cardiovascular diseases. Endothelial cells (ECs) lining the vascular lumen of capillaries are specialized, thin cells that supply tissues with metabolites, and their loss in aging poses a threat to tissue perfusion and ischemia risk. Mechanisms causing capillary dropout in aging are largely unknown, although VEGF signaling deficiency is implicated.15 To identify mechanisms involved in vascular aging, we investigated mice with the accelerated aging disorder Progeria, and aged wildtype mice. At the organismal level, Progerin expression reduced sprouting angiogenesis and capillary density in neonatal mouse retinas, which impaired tissue perfusion and increased DNA damage compared to wildtype littermates. Progeric capillaries and aortic ECs displayed a disrupted nuclear-cytoskeletal interface that was shared with aged wildtype mice. 4D EC sprouts derived from Progeria mice had mispositioned nuclei at sprouting tips and bulging nuclei that failed to flatten and obstructed lumen formation, demonstrating cell autonomous defects. Progerin expression in human ECs likewise affected nuclear positioning and flattening and prevented sprouting and lumen formation. Mechanistically, Progerin expression altered LINC complex protein abundance resulting in a failed connection with cytoskeletal Actin and the intermediate filament protein Vimentin. Impairment of the nuclear-cytoskeletal interface was conserved in Progeria patient derived ECs, and partially rescued by inhibiting Progerin with the drug, Progerinin. Taken together, our results reveal that nuclear positioning and flattening is required for angiogenic sprouting and lumen formation respectively and identify the nuclear-cytoskeletal interface as a vulnerability for age-related microvascular dropout and a target for therapeutic intervention.

cell biology↗

Conjugates of α-d-Galp-(1->3)-β-d-Galp for the serological diagnosis of Chagas disease.

BackgroundChagas disease (ChD), caused by the parasitic protozoan Trypanosoma cruzi, is a lifelong, neglected tropical disease with substantial medical and socioeconomic impact. Despite this situation, currently available diagnostic and therapeutic methods display serious limitations. A promising strategy to improve ChD serodiagnosis involves targeting parasite carbohydrate antigens, particularly the -galactosyl-rich mucins that coat the surface of bloodstream trypomastigotes (tGPI-mucins). Methods/Principle FindingsHere, we present a concise and efficient protocol for the chemical synthesis of a tGPI-mucin-derived glycotope, the disaccharide -O_SCPLOWDC_SCPLOW-Galp-(1[->]3)-{beta}-O_SCPLOWDC_SCPLOW-Galp, and its functional conjugation to different scaffolds using the squarate method. A neoglycoprotein made upon a bovine serum albumin (BSA) carrier decorated with [~]28 units of the disaccharide, termed BSA-Di, was interrogated with sera of chronic ChD patients and healthy individuals from Argentina using an in-house enzyme-linked immunosorbent assay (ELISA). BSA-Di exhibited excellent sensitivity and effectively discriminated between ChD-positive and negative sera with high accuracy (AUC = 0.905), though its specificity was partially affected by cross-reactivity of some non-ChD sera containing natural -Gal antibodies. Conjugation of -O_SCPLOWDC_SCPLOW-Galp-(1[->]3)-{beta}-O_SCPLOWDC_SCPLOW-Galp to T. cruzi antigenic peptides, instead of BSA, corroborated these findings and enabled the generation of bivalent ChD diagnostic reagents combining glycan- and peptide-based epitopes. Conclusions/SignificanceOverall, our results identify -O_SCPLOWDC_SCPLOW-Galp-(1[->]3)-{beta}-O_SCPLOWDC_SCPLOW-Galp as a robust and reliable biomarker of T. cruzi infection. The methodologies and tools described here, together with optimized derivatives, are expected to positively impact ChD serological applications. AUTHOR SUMMARYDespite the enormous burden imposed by Chagas disease, diagnostic and therapeutic methods still present serious deficiencies. Towards filling this gap, we herein developed a protocol for the chemical synthesis of -O_SCPLOWDC_SCPLOW-Galp-(1[->]3)-{beta}-O_SCPLOWDC_SCPLOW-Galp, a major glycotope present on the Trypanosoma cruzi surface coat. This disaccharide was conjugated with different molecular scaffolds and serologically evaluated using an in-house enzyme-linked immunosorbent assay (ELISA). Our results indicate that -O_SCPLOWDC_SCPLOW-Galp-(1[->]3)-{beta}-O_SCPLOWDC_SCPLOW-Galp provides an overall robust and reliable biomarker of T. cruzi infection, with excellent sensitivity and only minor concerns regarding its potential cross-reactivity with natural -Gal antibodies. These findings indicate that the tools developed here, as well as optimized versions derived from them, should have a positive impact on the diagnosis and clinical management of Chagas disease and on the identification and/or clinical validation of novel drug/vaccine candidates for the treatment of T. cruzi infections.

microbiology↗

Cell layer-specific modification of cell wall is associated with exo-mesocarp split in pistachio (Pistacia vera L.)

Pistachio (Pistacia vera) is a drought and salinity-tolerant perennial whose fruit features a fleshy exo-mesocarp, or "hull," that protects the kernel. Hull development and degradation are key to kernel quality, yet the anatomy and mechanisms driving hull breakdown during late-stage development remain largely unknown. Here, we show that the hull contains anatomically distinct layers of hypodermal parenchyma and filler parenchyma. Using a combination of transcriptome analyses and immunohistochemistry, we show that changes in pectin associated gene expression and modification of this polysaccharide are involved in hull cell size increase, loss of cell-cell adhesion, and softening. Anatomical analysis shows that filler parenchyma expands during late-stage hull development while hypodermal parenchyma remains constant in size. Field data suggest that irrigation and humidity affect pistachio hull split, implicating a role for water status in cell expansion. In summary, the complex interplay between molecular, cellular, and environmental changes suggests that cell layer-specific modifications of the cell wall are linked to exo-mesocarp splitting, forming a model for understanding the mechanism of fruit split during ripening in non-berry fruit crops. HighlightCell-layer specific modifications of the cell wall are associated with cell expansion and loss of cell-cell adhesion, leading to hull split during late-stage pistachio fruit development.

plant biology↗

Non-invasive measures of DNA methylation capture molecular aging in wild capuchin monkeys

Elucidating the socio-ecological factors that shape patterns of epigenetic modification in long-lived vertebrates is of broad interest to evolutionary biologists, geroscientists, and ecologists. However, aging research in wild populations is limited due to inability to measure cellular hallmarks of aging noninvasively. Here, we demonstrate that cellular DNA methylation (DNAm) profiles from fecal samples provide an accurate and reliable molecular clock in wild capuchin monkeys. Analysis of blood, feces, and urine samples from a closely related species shows that DNAm differentiates between species and different types of biological samples. We further find age-associated differences in DNAm relevant to cellular damage, inflammation, and senescence, consistent with hallmarks conserved across humans and other mammalian species, speaking to the comparative potential. By demonstrating that DNAm can be studied non-invasively in wild animals, our research opens new avenues in the study of modifiers of the pace of aging, and increases potential for cross-population and species comparisons.

genomics↗

Gene-embedding-based prediction and functional evaluation of perturbation expression responses with PRESAGE

Understanding the impact of genetic perturbations on cellular behavior is crucial for biological research, but comprehensive experimental mapping remains infeasible. We introduce PRESAGE (Perturbation Response EStimation with Aggregated Gene Embeddings), a simple, modular, and interpretable framework that predicts perturbation-induced expression changes by integrating diverse knowledge sources via gene embeddings. PRESAGE transforms gene embeddings through an attention-based model to predict perturbation expression outcomes. To assess model performance, we introduce a comprehensive evaluation suite with novel functional metrics that move beyond traditional regression tasks, including measures of accuracy in effect size prediction, in identifying perturbations with similar expression profiles (phenocopy), and in prediction of perturbations with the strongest impact on specific gene set scores. PRESAGE outperforms existing methods in both classical regression metrics and our novel functional evaluations. Through ablation studies, we demonstrate that knowledge source selection is more critical for predictive performance than architectural complexity, with cross-system Perturb-seq data providing particularly strong predictive power. We also find that performance saturates quickly with training set size, suggesting that experimental design strategies might benefit from collecting sparse perturbation data across multiple biological systems rather than exhaustive profiling of individual systems. Overall, PRESAGE establishes a robust framework for advancing perturbation response prediction and facilitating the design of targeted biological experiments, significantly improving our ability to predict cellular responses across diverse biological systems.

bioinformatics↗

SpatialAgent: An Autonomous AI Agent for Spatial Biology

Advances in AI are transforming scientific discovery, yet spatial biology, a field that deciphers the molecular organization within tissues, remains constrained by labor-intensive workflows. Here, we present SpatialAgent, an autonomous AI agent for spatial biology research. SpatialAgent couples large language models with a Plan-Act-Conclude architecture, dynamic tool and skill retrieval, multimodal interpretation, and verification modules that audit generated claims. It supports the full discovery loop, from gene-panel design and multimodal annotation to trajectory inference, cell-cell communication analysis, imputation, and hypothesis generation. Across human and mouse brain, heart, tonsil, colon, and prostate datasets, SpatialAgent outperformed established computational baselines and matched or surpassed expert scientists in key tasks. In open-ended case studies, it recovered known tissue organization and generated spatially grounded hypotheses. In a prospective mouse prostate cancer Xenium study, it designed a compact 100-gene add-on panel that profiled 4.2 million cells across 21 samples, improved cell-type and malignant-state prediction, and captured spatially structured tumor and microenvironment programs. SpatialAgent establishes a framework for autonomous and collaborative discovery in spatial biology.

bioinformatics↗

The role of innate immune responses against two strains of PEDV (S INDEL and non-S INDEL) in newborn and weaned piglets inoculated by combined orogastric and intranasal routes

Porcine epidemic diarrhea (PED) is a severe gastrointestinal disease in swine caused by PED virus (PEDV), leading to significant economic losses worldwide. Newborn piglets are especially vulnerable, with nearly 100% mortality, unlike older pigs. Disease severity also varies depending on the PEDV strain, with non-S INDEL strains being more virulent than S INDEL ones. This study examined early pathogenesis and innate immunity in 1-week-old suckling and 5- week-old weaned piglets (n=8 per age group, 4 per strain) inoculated with S INDEL or non-S INDEL PEDV strains via combined orogastric and intranasal route. Age-matched negative controls (n=3 per age group) were included. Body weight, temperature, and clinical signs were monitored for 48 hours post-inoculation (hpi). PEDV RNA levels were assessed in rectal swabs (RS) at 0 and 48 hpi, while pathological analyses and viral RNA loads were measured in jejunal content and intestinal mucosa. Gene expression of 75 selected antiviral and inflammatory genes were measured in laser capture microdissection (LCM)-derived jejunal samples using microfluidic qPCR at 48 hpi. Suckling piglets showed severe clinical signs, while weaned piglets were mostly asymptomatic at 48 hpi. In general, clinical signs and lesions in suckling piglets were similar, regardless of the PEDV strain. Both viral strains produced comparable viral RNA loads in the small intestine and feces, as well as consistent villous atrophy and fusion across age groups. In LCM-derived jejunal samples, weaned piglets had higher expression of antiviral genes (type I/III interferons, ISGs) and Th1/Th17 pro-inflammatory genes, particularly with the non-S INDEL strain. Conversely, the anti-inflammatory cytokine IL-10 was overexpressed in suckling compared to weaned piglets for both strains. Overall, PEDV-induced intestinal damage, viral replication, and excretion were similar regardless of viral strain or piglet age. The reduced clinical severity in weaned piglets may result from their stronger intestinal antiviral and pro-inflammatory response. AUTHOR SUMMARYPorcine epidemic diarrhea virus (PEDV) is the causative agent of a major gastrointestinal disease in piglets worldwide, characterized by severe watery diarrhea. The disease is particularly devastating in newborn piglets, especially when caused by non-S INDEL PEDV strains, while weaned piglets demonstrate resistance regardless of the strain. In this study, during the acute infection phase (48 hpi), both highly virulent non-S INDEL and less virulent S INDEL strains caused comparable intestinal atrophy, viral replication in the intestine, and viral loads in feces in both weaned and suckling piglets. However, weaned piglets mounted a robust antiviral response involving type I and III interferons (IFNs) and the induction of Th1- and Th17-related pro-inflammatory responses in the intestinal mucosa. Additionally, interferon-stimulated genes (ISGs) were broadly upregulated across the intestinal mucosa of weaned piglets in response to both PEDV strains. In contrast, suckling piglets exhibited a weaker innate immune response, coinciding with more severe clinical signs. The observed inverse relationship between disease severity and intestinal innate immune activation highlights the potential role of IFNs, ISGs, and pro-inflammatory cytokines in mitigating PEDV severity and underscores their relevance in developing novel pharmacological prevention strategies.

immunology↗

Degron-modified Cas12a enhances single-cell CRISPR screening

Single-cell CRISPR (Perturb-seq) screens have primarily relied on Cas9 whereas Cas12a, despite its unique effectiveness for multiplex guide expression, remains underexplored. This may be due to Cas12as guide RNA array (pre-crRNA) self-processing activity and the subsequent challenges associated with pre-crRNA sequence recovery. By developing modified pre-crRNA expression vectors and a degron-based Cas12a system, we overcome the self-processing constraint, allowing for accurate detection of pre-crRNAs at the single-cell level, thus greatly expanding possibilities for future Perturb-seq efforts.

molecular biology↗

Metabolic Adaptations in Adult Spiny Mouse (Acomys) Cardiomyocytes Facilitate Enhanced Cardiac Recovery Following Myocardial Infarction

The adult mammalian heart has limited regenerative capacity following injury, leading to progressive heart failure and mortality. Recent studies have identified the spiny mouse (Acomys) as a unique model for mammalian cardiac regeneration, exhibiting enhanced recovery after myocardial infarction compared to commonly used laboratory mouse strains. However, the cellular and molecular mechanisms underlying this regenerative response remain poorly understood. In this study, we performed a comprehensive characterization of the metabolic adaptations to ischemic injury in cardiomyocytes of Acomys in comparison to the non-regenerative Mus Musculus. To investigate the transcriptomic and metabolomic profiles of cardiomyocytes in response to myocardial infarction, we utilized single-nucleus RNA sequencing (snRNA-seq) in sham-operated animals and 1, 3, and 7 days post-myocardial infarction. Complementary targeted metabolomics, stable isotope-resolved metabolomics, and functional mitochondrial assays were performed on heart tissues from both species to validate the transcriptomic findings and elucidate the metabolic adaptations in cardiomyocytes following ischemic injury. Transcriptomic analysis revealed that Acomys cardiomyocytes upregulate genes associated with glycolysis, the pentose phosphate pathway, and glutathione metabolism while downregulating genes involved in oxidative phosphorylation following injury. These metabolic changes were linked to decreased production of reactive oxygen species and increased antioxidant capacity, evidenced by the upregulation of genes such as Prdx1, Sod1, Sod2, and G6pd. Our targeted metabolomic studies supported these findings, showing a shift from fatty acid oxidation to glycolysis and ancillary biosynthetic pathways in Acomys cardiomyocytes post-injury. Functional mitochondrial studies indicated a higher reliance on glycolysis in Acomys compared to Mus, underscoring the unique metabolic adaptations of Acomys cardiomyocytes. Stable isotope tracing experiments confirmed a shift in glucose utilization from oxidative phosphorylation in Acomys. In conclusion, our study identifies unique metabolic adaptations in Acomys cardiomyocytes that contribute to their enhanced regenerative capacity following myocardial infarction. These findings provide novel insights into the role of metabolism in regulating cardiomyocyte proliferation and cardiac repair in adult mammals. By targeting the specific metabolic pathways and regulators identified in Acomys, such as glycolytic enzymes and PCK2, we may be able to develop innovative therapies to promote cardiac regeneration in patients with ischemic heart disease. Our work highlights the importance of metabolic flexibility in determining cardiomyocyte regenerative responses and establishes Acomys as a valuable model for studying cardiac regeneration in adult mammals. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/595229v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@dba53aorg.highwire.dtl.DTLVardef@3c8a74org.highwire.dtl.DTLVardef@a60251org.highwire.dtl.DTLVardef@e2142c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

A Supervised Contrastive Framework for Learning Disentangled Representations of Cell Perturbation Data

CRISPR technology, combined with single-cell RNA-Seq, has opened the way to large scale pooled perturbation screens, allowing more systematic interrogations of gene functions in cells at scale. However, such Perturb-seq data poses many analysis challenges, due to its high-dimensionality, high level of technical noise, and variable Cas9 efficiency. The single-cell nature of the data also poses its own challenges, as we observe the heterogeneity of phenotypes in the unperturbed cells, along with the effect of the perturbations. All in all, these characteristics make it difficult to discern subtler effects. Existing tools, like mixscape and ContrastiveVI, provide partial solutions, but may oversimplify biological dynamics, or have low power to characterize perturbations with a smaller effect size. Here, we address these limitations by introducing the Supervised Contrastive Variational Autoencoder (SC-VAE). SC-VAE integrates guide RNA identity with gene expression data, ensuring a more discriminative analysis, and adopts the Hilbert-Schmidt Independence Criterion as a way to achieve disentangled representations, separating the heterogeneity in the control population from the effect of the perturbations. Evaluation on large-scale data sets highlights SC-VAEs superior sensitivity in identifying perturbation effects compared to ContrastiveVI, scVI and PCA. The perturbation embeddings better reflect known protein complexes (evaluated on CORUM), while its classifier offers promise in identifying assignment errors and cells escaping the perturbation phenotype. SC-VAE is readily applicable across diverse perturbation data sets.

bioinformatics↗

Sequential Optimal Experimental Design of Perturbation Screens Guided by Multi-modal Priors

Understanding a cells expression response to genetic perturbations helps to address important challenges in biology and medicine, including the function of gene circuits, discovery of therapeutic targets and cell reprogramming and engineering. In recent years, Perturb-seq, pooled genetic screens with single cell RNA-seq (scRNA-seq) readouts, has emerged as a common method to collect such data. However, irrespective of technological advances, because combinations of gene perturbations can have unpredictable, non-additive effects, the number of experimental configurations far exceeds experimental capacity, and for certain cases, the number of available cells. While recent machine learning models, trained on existing Perturb-seq data sets, can predict perturbation outcomes with some degree of accuracy, they are currently limited by sub-optimal training set selection and the small number of cell contexts of training data, leading to poor predictions for unexplored parts of perturbation space. As biologists deploy Perturb-seq across diverse biological systems, there is an enormous need for algorithms to guide iterative experiments while exploring the large space of possible perturbations and their combinations. Here, we propose a sequential approach for designing Perturb-seq experiments that uses the model to strategically select the most informative perturbations at each step for subsequent experiments. This enables a significantly more efficient exploration of the perturbation space, while predicting the effect of the rest of the unseen perturbations with high-fidelity. Analysis of a previous large-scale Perturb-seq experiment reveals that our setting is severely restricted by the number of examples and rounds, falling into a non-conventional active learning regime called "active learning on a budget". Motivated by this insight, we develop IO_SCPLOWTERC_SCPLOWPO_SCPLOWERTC_SCPLOW, a novel active learning method that exploits rich and multi-modal prior knowledge in order to efficiently guide the selection of subsequent perturbations. Using prior knowledge for this task is novel, and crucial for successful active learning on a budget. We validate IO_SCPLOWTERC_SCPLOWPO_SCPLOWERTC_SCPLOW using insilico benchmarking of active learning, constructed from a large-scale CRISPRi Perturb-seq data set. We find that IO_SCPLOWTERC_SCPLOWPO_SCPLOWERTC_SCPLOW outperforms other active learning strategies by reaching comparable accuracy at only a third of the number of perturbations profiled as the next best method. Overall, our results demonstrate the potential of sequentially designing perturbation screens through IO_SCPLOWTERC_SCPLOWPO_SCPLOWERTC_SCPLOW.

bioinformatics↗

Multi-ContrastiveVAE disentangles perturbation effects in single cell images from optical pooled screens

Optical pooled screens (OPS) enable comprehensive and cost-effective interrogation of gene function by measuring microscopy images of millions of cells across thousands of perturbations. However, the analysis of OPS data still mainly relies on hand-crafted features, even though these are difficult to deploy across complex data sets. This is because most unsupervised feature extraction methods based on neural networks (such as auto-encoders) have difficulty isolating the effect of perturbations from the natural variations across cells and experimental batches. Here, we propose a contrastive analysis framework that can more effectively disentangle the phenotypes caused by perturbation from natural cell-cell heterogeneity present in an unperturbed cell population. We demonstrate this approach by analyzing a large data set of over 30 million cells imaged across more than 5, 000 genetic perturbations, showing that our method significantly outperforms traditional approaches in generating biologically-informative embeddings and mitigating technical artifacts. Furthermore, the interpretable part of our model distinguishes perturbations that generate novel phenotypes from the ones that only shift the distribution of existing phenotypes. Our approach can be readily applied to other small-molecule and genetic perturbation data sets with highly multiplexed images, enhancing the efficiency and precision in identifying and interpreting perturbation-specific phenotypic patterns, paving the way for deeper insights and discoveries in OPS analysis.

bioinformatics↗

Unveiling the antiviral capabilities of targeting Human Dihydroorotate Dehydrogenase against SARS-CoV-2

The urgent need for effective treatments against emerging viral diseases, driven by drug-resistant strains and new viral variants, remains critical. We focus on inhibiting the human dihydroorotate dehydrogenase (HsDHODH), one of the enzymes in charge of pyrimidine nucleotide synthesis. This strategy could impede viral replication without provoking resistance. We evaluated quinone-based compounds, discovering potent HsDHODH inhibition (low nanomolar IC50) and promising in vitro anti-SARS-CoV-2 activity (low micromolar EC50). These compounds exhibited low toxicity, indicating potential for further development. Additionally, we employed computational tools like molecular docking and QSAR models to analyze protein-ligand interactions. These findings represent a significant step forward in the search for effective antiviral treatments and have great potential to impact the development of new broad-spectrum antiviral drugs.

biochemistry↗