High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.

The identity of a cell is governed by regulatory proteins binding to the genome to control gene expression. Mapping these genome-wide binding events across thousands of proteins and cell types is essential for understanding development and disease at scale, yet has remained a major experimental and computational barrier. Here we present Chromnitron, a multimodal foundation model that learns the rules of protein-DNA binding from protein sequence, DNA sequence, and context-specific chromatin states. Unlike prior single-task and multi-task learning approaches, Chromnitron implements a multimodal learning framework that accurately predicts the binding landscape for proteins and cell types not seen during training. Using Chromnitron, we discovered and experimentally validated new protein regulators of T cell exhaustion. Chromnitron also uncovered previously uncharacterized dynamic shifts in the binding landscape of regulatory proteins during neurogenesis. This marks a critical step toward a predictive model of interpretable gene regulatory programs across cell types, enabling rapid discovery of regulatory circuits and identification of new therapeutic targets.

Chromatin-associated proteins (CAPs), including over 1,600 transcription factors, bind directly or indirectly to the genomic DNA to regulate gene expression and determine a myriad of cell types. Mapping their genome-wide binding and co-binding landscape is essential towards a mechanistic understanding of their functions in gene regulation and resulting cellular phenotypes. However, due to the lack of techniques that effectively scale across proteins and biological samples, their genome-wide binding profiles remain challenging to obtain, particularly in primary cells. Here we present Chromnitron, a multimodal foundation model that accurately predicts CAP binding landscapes across hundreds of proteins in unseen cell types. Via in silico perturbation experiments, we show that the model learned principles of CAP binding from multimodal features including DNA sequence motifs, chromatin accessibility levels, and protein functional domains. Applying Chromnitron to study cell fate transitions, we discovered novel CAPs regulating the T cell exhaustion process. Furthermore, Chromnitron can predict the dynamic CAP binding landscapes during development, revealing the global orchestration of protein and regulatory element activities in neurogenesis. We expect Chromnitron to accelerate discovery and engineering in regulatory genomics, particularly in human primary cells, and empower future therapeutic opportunities.

The vertebrate genome is spatially organized into topologically associating domains (TADs), primarily via cohesin-mediated loop extrusion which typically halts at convergent CTCF binding sites to establish domain boundaries. However, despite the essential roles of CTCF and cohesin in establishing TADs, a long-standing paradox persists: CTCF and cohesin binding sites dramatically outnumber observed TAD boundaries, suggesting the existence of undiscovered architectural factors. To identify such missing factors, we conducted high-resolution in silico screens using C.Origami, a multi-modal AI model for predicting chromatin interactions. Remarkably, we identified ZNF654 and JMJD6 as novel factors uniquely defining TAD boundaries. Experimental validation confirmed that ZNF654, an uncharacterized vertebrate-specific zinc-finger protein, interacts with CTCF to form an architectural protein complex that demarcates chromatin domains. Genetic knockout of ZNF654 weakens TAD boundary strength without influencing other CTCF or cohesin binding sites. JMJD6, a deeply conserved jmjC-family dioxygenase, marks the anchors of the strongest chromatin stripes at both TAD boundaries and enhancer-promoter sites, while deleting JMJD6 weakens or diminishes such interaction signature. These results revealed the long-sought factors that uniquely mark TAD boundary and chromatin interaction anchors which, together with CTCF and cohesin, demarcate chromatin domains during 3D genome organization. Last, the evolutionary trajectory of ZNF654 and JMJD6 offers key insight into the evolutionary origins of 3D genome organization across metazoan species.

Microsporidia are single-celled intracellular parasites that cause opportunistic diseases in humans. Encephalitozoon intestinalis is a prevalent human-infecting species that invades the small intestine. Macrophages are potential reservoirs of infection, and dissemination to other organ systems is also observed. The macrophage response to infection and the developmental trajectory of the parasite are not well studied. Here we use single cell RNA sequencing to investigate transcriptional changes in both the parasite and the host during E. intestinalis infection of human macrophages in vitro. The parasite undergoes large transcriptional changes throughout the life cycle, providing a blueprint for parasite development. While a small population of infected macrophages mount a response, most remain transcriptionally unchanged, suggesting that the majority of parasites may avoid host detection. The stealthy microsporidian lifestyle likely allows these parasites to harness macrophages for replication. Together, our data provide insights into the host response in primary human macrophages and the E. intestinalis developmental program.

In a previous study, heart xenografts from 10-gene-edited pigs transplanted into two human decedents did not show evidence of acute-onset cellular- or antibody-mediated rejection. Here, to better understand the detailed molecular landscape following xenotransplantation, we carried out bulk and single-cell transcriptomics, lipidomics, proteomics and metabolomics on blood samples obtained from the transplanted decedents every 6 h, as well as histological and transcriptomic tissue profiling. We observed substantial early immune responses in peripheral blood mononuclear cells and xenograft tissue obtained from decedent 1 (male), associated with downstream T cell and natural killer cell activity. Longitudinal analyses indicated the presence of ischemia reperfusion injury, exacerbated by inadequate immunosuppression of T cells, consistent with previous findings of perioperative cardiac xenograft dysfunction in pig-to-nonhuman primate studies. Moreover, at 42 h after transplantation, substantial alterations in cellular metabolism and liver-damage pathways occurred, correlating with profound organ-wide physiological dysfunction. By contrast, relatively minor changes in RNA, protein, lipid and metabolism profiles were observed in decedent 2 (female) as compared to decedent 1. Overall, these multi-omics analyses delineate distinct responses to cardiac xenotransplantation in the two human decedents and reveal new insights into early molecular and immune responses after xenotransplantation. These findings may aid in the development of targeted therapeutic approaches to limit ischemia reperfusion injury-related phenotypes and improve outcomes. Multi-omics profiling of the blood and heart of two human decedents receiving pig heart xenografts, including single-cell studies, reveals early immune responses and perioperative cardiac xenograft dysfunction in one of the two decedents, which may be due to mismatched heart size and/or insufficient immunosuppression.

BACKGROUND:Xenotransplantation of genetically engineered porcine organs has the potential to address the challenge of organ donor shortage. Two cases of porcine-to-human kidney xenotransplantation were performed, yet the physiological effects on the xenografts and the recipients' immune responses remain largely uncharacterized. METHODS:We performed single-cell RNA sequencing (scRNA-seq) and longitudinal RNA-seq analyses of the porcine kidneys to dissect xenotransplantation-associated cellular dynamics and xenograft-recipient interactions. We additionally performed longitudinal scRNA-seq of the peripheral blood mononuclear cells (PBMCs) to detect recipient immune responses across time. FINDINGS:Although no hyperacute rejection signals were detected, scRNA-seq analyses of the xenografts found evidence of endothelial cell and immune response activation, indicating early signs of antibody-mediated rejection. Tracing the cells' species origin, we found human immune cell infiltration in both xenografts. Human transcripts in the longitudinal bulk RNA-seq revealed that human immune cell infiltration and the activation of interferon-gamma-induced chemokine expression occurred by 12 and 48 h post-xenotransplantation, respectively. Concordantly, longitudinal scRNA-seq of PBMCs also revealed two phases of the recipients' immune responses at 12 and 48-53 h. Lastly, we observed global expression signatures of xenotransplantation-associated kidney tissue damage in the xenografts. Surprisingly, we detected a rapid increase of proliferative cells in both xenografts, indicating the activation of the porcine tissue repair program. CONCLUSIONS:Longitudinal and single-cell transcriptomic analyses of porcine kidneys and the recipient's PBMCs revealed time-resolved cellular dynamics of xenograft-recipient interactions during xenotransplantation. These cues can be leveraged for designing gene edits and immunosuppression regimens to optimize xenotransplantation outcomes. FUNDING:This work was supported by NIH RM1HG009491 and DP5OD033430.

The loss of the tail is one of the main anatomical evolutionary changes to have occurred along the lineage leading to humans and to the “anthropomorphous apes”1,2. This morphological reprogramming in the ancestral hominoids has been long considered to have accommodated a characteristic style of locomotion and contributed to the evolution of bipedalism in humans3–5. Yet, the precise genetic mechanism that facilitated tail-loss evolution in hominoids remains unknown. Primate genome sequencing projects have made possible the identification of causal links between genotypic and phenotypic changes6–8, and enable the search for hominoid-specific genetic elements controlling tail development9. Here, we present evidence that tail-loss evolution was mediated by the insertion of an individual Alu element into the genome of the hominoid ancestor. We demonstrate that this Alu element – inserted into an intron of the TBXT gene (also called T or Brachyury10–12) – pairs with a neighboring ancestral Alu element encoded in the reverse genomic orientation and leads to a hominoid-specific alternative splicing event. To study the effect of this splicing event, we generated a mouse model that mimics the expression of human TBXT products by expressing both full-length and exon-skipped isoforms of the mouse TBXT ortholog. We found that mice with this genotype exhibit the complete absence of a tail or a shortened tail, supporting the notion that the exon-skipped transcript is sufficient to induce a tail-loss phenotype, albeit with incomplete penetrance. We further noted that mice homozygous for the exon-skipped isoforms exhibited embryonic spinal cord malformations, resembling a neural tube defect condition, which affects ∼1/1000 human neonates13. We propose that selection for the loss of the tail along the hominoid lineage was associated with an adaptive cost of potential neural tube defects and that this ancient evolutionary trade-off may thus continue to affect human health today.

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

ABSTRACT Background Recent advances in xenotransplantation in living and decedent humans using pig xenografts have laid promising groundwork towards future emergency use and first in human trials. Major obstacles remain though, including a lack of knowledge of the genetic incompatibilities between pig donors and human recipients which may led to harmful immune responses against the xenograft or dysregulation of normal physiology. In 2022 two pig heart xenografts were transplanted into two brain-dead human decedents with a minimized immunosuppression regime, primarily to evaluate onset of hyper-acute antibody mediated rejection and sustained xenograft function over 3 days. Methods We performed multi-omic profiling to assess the dynamic interactions between the pig and human genomes in the first two pig heart-xenografts transplants into human decedents. To assess global and specific biological changes that may correlate with immune-related outcomes and xenograft function, we generated transcriptomic, lipidomic, proteomic and metabolomics datasets, across blood and tissue samples collected every 6 hours over the 3-day procedures. Results Single-cell datasets in the 3-day pig xenograft-decedent models show dynamic immune activation processes. We observe specific scRNA-seq, snRNA-seq and geospatial transcriptomic changes of early immune-activation leading to pronounced downstream T-cell activity and hallmarks of early antibody mediated rejection (AbMR) and/or ischemia reperfusion injury (IRI) in the first xenograft recipient. Using longitudinal multiomic integrative analyses from blood in addition to antigen presentation pathway enrichment, we also observe in the first xeno-heart recipient significant cellular metabolism and liver damage pathway changes that correlate with profound physiological dysfunction whereas, these signals are not present in the other xenograft recipient. Conclusions Single-cell and multiomics approaches reveal fundamental insights into early molecular immune responses indicative of IRI and/or early AbMR in the first human decedent, which was not evident in the conventional histological evaluations.

Investigating how chromatin organization determines cell-type-specific gene expression remains challenging. Experimental methods for measuring three-dimensional chromatin organization, such as Hi-C, are costly and have technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. We present C.Origami, a multimodal deep neural network that performs de novo prediction of cell-type-specific chromatin organization using DNA sequence and two cell-type-specific genomic features—CTCF binding and chromatin accessibility. C.Origami enables in silico experiments to examine the impact of genetic changes on chromatin interactions. We further developed an in silico genetic screening approach to assess how individual DNA elements may contribute to chromatin organization and to identify putative cell-type-specific trans -acting regulators that collectively determine chromatin architecture. Applying this approach to leukemia cells and normal T cells, we demonstrate that cell-type-specific in silico genetic screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulation circuits in both normal and disease-related biological systems.

Introduction: Pig organs offer significant advantages for transplant into humans, and may alleviate the current critical shortage of suitable organs. Advances in genetic knockout models of key xeno-antigens such as α-1,3-Gal and ethical research using recently deceased brain-dead human donors has enabled the first sets of xenotransplants (XTx) to be performed. In the Summer of 2022 two pig heart xenografts were transplanted into two human decedents in NYU for ~3 days, with the primary aims being to assess hyper-acute rejection and appropriate xenograft functioning. We performed multi-omic profiling to assess potential xenogenic interactions between pig and human genomes, and dynamic biological interactions across blood samples to assess immunological and other physiological changes. Methods: Short-read whole-genome sequencing (WGS) was performed across the human and pig genomes. Bulk FFPE RNAseq in peripheral blood mononuclear cells (PBMCs) and biopsy tissues, and Single-cell RNA-sequencing (scRNAseq) of PBMCs were performed every 6 hours on up to 25 timepoints across the two xenoheart procedures. Proteomics in plasma was performed using liquid chromatography mass spec (LC-MS), PECAN, SWATH-MS. Metabolomics was performed using gas chromatography (GC)-MS & LC-MS. Conventional, digital and electron microscopy imaging of 30 biopsies was also performed. Results: Multi-omic profiling of blood from two pig heart to human xenotransplants shows significant changes increases in metabolic and antigen presentation pathways after the 40 hours timepoints in the first xenoheart decedent (PHX1 in Figure 1). This timeframe coincides with significant physiological changes including major increases in liver transaminase enzyme activity. Significant proteomic and metabolomic pathway changes were observed including Glycolysis/Gluconeogenesis metabolism (p< 5 x 10-20), Pyruvate metabolism (p < 7 x 10-11) and antigen processing and presentation (p = 5.6 x 10-5). Utilizing PBMC scRNAseq across the same time points, we identify key changes in immune cell types and subtypes, including T-cell subtypes, B cells, granulocytes, macrophages, and monocytes. Notably we see a particularly sharp increase in CD4 T-cells, followed by CD8 T-cells with these changes preceded by a marked increase in B cells, whose timepoint-associated phenotypes exhibit IL1B- & IL17-related activation at 6 and 12 hours, followed by B-cell proliferation and BCR activation at 18 and 24 hours. Conclusion: Multi-omic profiling including scRNAseq profiling in two pig to human xeno heart-transplant decedent datasets show significant changes in metabolic and immune function after 40 hours in the first decedent subject.

Background: Xenografts from genetically modified pigs have become one of the most promising solutions to the human organ shortage. Humans lack the α1,3-Gal glycan ubiquitously expressed on pig cells. Naturally occurring anti-α1,3-Gal antibodies can cause hyperacute rejection of porcine xenografts. Cytokines levels change in response to systemic inflammatory or transplant environments. We report the first instance of cytokine analysis from xenotransplantation of a kidney from an α1,3-Gal knock-out (KO) pig to a recently deceased human. Methods: Under a protocol approved by the NYU institutional committee two xenotransplants were performed in late 2021 on brain-dead recipients who consented their bodies for organ and research donation, but were found to have organs unsuitable for transplantation. Kidneys from α1,3-Gal KO pigs were transplanted into the thigh of the recipient. Immunosuppression consisted of methylprednisolone and mycophenolate mofetil until the kidneys were explanted at 54 hours. Cytokine analysis was performed as well as genetic expression for cytokines in the blood and for the second transplant biopsy tissue as well. Blood cytokines were measured in the NYU clinical laboratory. Xenotransplant tissue was obtained by needle biopsy and mRNA extracted for analysis. Results: The xenografts immediately appeared pink and well-perfused and began to make urine. The cold ischemic times were 7 and 6 hours, respectively. Most cytokines showed no change or were undetectable in the blood. Of the detectable cytokines, IL-2 mRNA levels decreased in porcine kidney biopsy samples over the course of the second transplant but increased in the blood (this recipient had a positive CDC xenocrossmatch). Figure 1Blood IL-6 levels were elevated pre-transplant and decreased with time, whereas porcine kidney mRNA levels rose to a peak 12 hours post-transplant and then gradually decreased. Conclusions: Our study served as proof of concept that α1,3-Gal KO organs can be transplanted into humans without the risk of accelerated rejection and cytokine storm. Further analysis and correlation between xenotransplant porcine tissue biopsy and circulating blood levels is warranted in future pre-clinical studies. Research grant support was provided by Lung Biotechnology PBC, a wholly owned subsidiary of United Therapeutics. The pig kidneys were obtained from Revivicor, Inc., a subsidiary of United Therapeutics.

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.

The mammalian genome is spatially organized in the nucleus to enable cell type-specific gene expression. Investigating how chromatin architecture determines this specificity remains a big challenge. Methods for measuring the 3D chromatin architecture, such as Hi-C, are costly and bears strong technical limitations, restricting their widespread application particularly when concerning genetic perturbations. In this study, we present C.Origami, a deep neural network model for predicting de novo cell type-specific chromatin architecture. By incorporating DNA sequence, CTCF binding, and chromatin accessibility profiles, C.Origami achieves accurate cell type-specific prediction. C.Origami enables in silico experiments that examine the impact of genetic perturbations on chromatin interactions, and moreover, leads to the identification of a compendium of cell type-specific regulators of 3D chromatin architecture. We expect Origami – the underlying model architecture of C.Origami – to be generalizable for future genomics studies in discovering novel regulatory mechanisms of the genome.

ABSTRACTWhile genetic tumor heterogeneity has long been recognized, recent work has revealed significant variation among cancer cells at the epigenetic and transcriptional levels. Profiling tumors at the single-cell level in individual cancer types has shown that transcriptional heterogeneity is organized into cancer cell states, implying that diverse cell states may represent stable and functional units with complementary roles in tumor maintenance and progression. However, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Furthermore, the role of cancer cell states in tumor progression and their specific interactions with cells of the tumor microenvironment remain to be elucidated. Here, we perform a pan-cancer single-cell RNA-Seq analysis across 15 cancer types and identify a catalog of 16 gene modules whose expression defines recurrent cancer cell states, including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Using mouse models, we find that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Moreover, spatial transcriptomic analysis further links the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance, and metastasis.

Of all mammalian organs, the testis has long been observed to have the most diverse gene expression profile. To account for this widespread gene expression, we have proposed a mechanism termed 'transcriptional scanning', which reduces germline mutation rates through transcription-coupled repair (TCR). Our hypothesis contrasts with an earlier observation that mutation rates are overall positively correlated with gene expression levels in yeast, implying that transcription is mutagenic due to effects dominated by transcription-coupled damage (TCD). Here we report evidence that the compound effects of both TCR and TCD during spermatogenesis modulate human germline mutation rates, with TCR dominating in most genes, thus supporting the transcriptional scanning hypothesis. Our analyses address potentially confounding factors, distinguish the differential mutagenic effects acting on the highly expressed genes and the low-to-moderately expressed genes, and resolve concerns relating to the validation of the results using a de novo mutation dataset. We also discuss the theoretical possibility of transcriptional scanning hypothesis from an evolutionary perspective. Together, these analyses support a model by which the coupling of transcription-coupled repair and damage establishes the pattern of germline mutation rates and provide an evolutionary explanation for widespread gene expression during spermatogenesis.

The testis expresses the largest number of genes of any mammalian organ, a finding that has long puzzled molecular biologists. Analyzing our single-cell transcriptomic maps of human and mouse spermatogenesis, we provide evidence that this widespread transcription serves to maintain DNA sequence integrity in the male germline by correcting DNA damage through 'transcriptional scanning'. Supporting this model, we find that genes expressed during spermatogenesis display lower mutation rates on the transcribed strand and have low diversity in the population. Moreover, this effect is fine-tuned by the level of gene expression during spermatogenesis. The unexpressed genes, which in our model do not benefit from transcriptional scanning, diverge faster over evolutionary time-scales and are enriched for sensory and immune-defense functions. Collectively, we propose that transcriptional scanning modulates germline mutation rates in a gene-specific manner, maintaining DNA sequence integrity for the bulk of genes but allowing for fast evolution in a specific subset.

Single cell biology is currently revolutionizing developmental and evolutionary biology, revealing new cell types and states in an impressive range of biological systems. With the accumulation of data, however, the field is grappling with a central unanswered question: what exactly is a cell type? This question is further complicated by the inherently dynamic nature of developmental processes. In this Hypothesis article, we propose that a 'periodic table of cell types' can be used as a framework for distinguishing cell types from cell states, in which the periods and groups correspond to developmental trajectories and stages along differentiation, respectively. The different states of the same cell type are further analogous to 'isotopes'. We also highlight how the concept of a periodic table of cell types could be useful for predicting new cell types and states, and for recognizing relationships between cell types throughout development and evolution.