The CD4 Versus CD8 T Cell Fate Decision: A Multiomics-Informed Perspective.

The choice of developing thymocytes to become CD8+ cytotoxic or CD4+ helper T cells has been intensely studied, but many of the underlying mechanisms remain to be elucidated. Recent multiomics approaches have provided much higher resolution analysis of gene expression in developing thymocytes than was previously achievable, thereby offering a fresh perspective on this question. Focusing on our recent studies using CITE-seq (cellular indexing of transcriptomes and epitopes) analyses of mouse thymocytes, we present a detailed timeline of RNA and protein expression changes during CD8 versus CD4 T cell differentiation. We also revisit our current understanding of the links between T cell receptor signaling and expression of the lineage-defining transcription factors ThPOK and RUNX3. Finally, we propose a sequential selection model to explain the tight linkage between MHC-I versus MHC-II recognition and T cell lineage choice. This model incorporates key aspects of previously proposed kinetic signaling, instructive, and stochastic/selection models.

The Myeloid Cell Compartment-Cell by Cell.

Myeloid cells are a major cellular compartment of the immune system comprising monocytes, dendritic cells, tissue macrophages, and granulocytes. Models of cellular ontogeny, activation, differentiation, and tissue-specific functions of myeloid cells have been revisited during the last years with surprising results; for example, most tissue macrophages are yolk sac derived, monocytes and macrophages follow a multidimensional model of activation, and tissue signals have a significant impact on the functionality of all these cells. While these exciting results have brought these cells back to center stage, their enormous plasticity and heterogeneity, during both homeostasis and disease, are far from understood. At the same time, the ongoing revolution in single-cell genomics, with single-cell RNA sequencing (scRNA-seq) leading the way, promises to change this. Prevailing models of hematopoiesis with distinct intermediates are challenged by scRNA-seq data suggesting more continuous developmental trajectories in the myeloid cell compartment. Cell subset structures previously defined by protein marker expression need to be revised based on unbiased analyses of scRNA-seq data. Particularly in inflammatory conditions, myeloid cells exhibit substantially vaster heterogeneity than previously anticipated, and work performed within large international projects, such as the Human Cell Atlas, has already revealed novel tissue macrophage subsets. Based on these exciting developments, we propose the next steps to a full understanding of the myeloid cell compartment in health and diseases.

Reading and Writing the Human Glycocode.

The complex carbohydrate structures decorating human proteins and lipids, also called glycans, are abundantly present at cell surfaces and in the secretome. Glycosylation is vital for biological processes including cell-cell recognition, immune responses, and signaling pathways. Therefore, the structural and functional characterization of the human glycome is gaining more and more interest in basic biochemistry research and in the context of developing new therapies, diagnostic tools, and biotechnology applications. For glycomics to reach its full potential in these fields, it is critical to appreciate the specific factors defining the function of the human glycome. Here, we review the glycosyltransferases (the writers) that form the glycome and the glycan-binding proteins (the readers) with an essential role in decoding glycan functions. While abundantly present throughout different cells and tissues, the function of specific glycosylation features is highly dependent on their context. In this review, we highlight the relevance of studying the glycome in the context of specific carrier proteins, cell types, and subcellular locations. With this, we hope to contribute to a richer understanding of the glycome and a more systematic approach to identifying the roles of glycosylation in human physiology.

The future of rapid and automated single-cell data analysis using reference mapping.

As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.

Intestinal Blastocystis is linked to healthier diets and more favorable cardiometabolic outcomes in 56,989 individuals from 32 countries.

Diet impacts human health, influencing body adiposity and the risk of developing cardiometabolic diseases. The gut microbiome is a key player in the diet-health axis, but while its bacterial fraction is widely studied, the role of micro-eukaryotes, including Blastocystis, is underexplored. We performed a global-scale analysis on 56,989 metagenomes and showed that human Blastocystis exhibits distinct prevalence patterns linked to geography, lifestyle, and dietary habits. Blastocystis presence defined a specific bacterial signature and was positively associated with more favorable cardiometabolic profiles and negatively with obesity (p < 1e-16) and disorders linked to altered gut ecology (p < 1e-8). In a diet intervention study involving 1,124 individuals, improvements in dietary quality were linked to weight loss and increases in Blastocystis prevalence (p = 0.003) and abundance (p < 1e-7). Our findings suggest a potentially beneficial role for Blastocystis, which may help explain personalized host responses to diet and downstream disease etiopathogenesis.

Pan-cancer single-cell dissection reveals phenotypically distinct B cell subtypes.

Characterizing the compositional and phenotypic characteristics of tumor-infiltrating B cells (TIBs) is important for advancing our understanding of their role in cancer development. Here, we establish a comprehensive resource of human B cells by integrating single-cell RNA sequencing data of B cells from 649 patients across 19 major cancer types. We demonstrate substantial heterogeneity in their total abundance and subtype composition and observe immunoglobulin G (IgG)-skewness of antibody-secreting cell isotypes. Moreover, we identify stress-response memory B cells and tumor-associated atypical B cells (TAABs), two tumor-enriched subpopulations with prognostic potential, shared in a pan-cancer manner. In particular, TAABs, characterized by a high clonal expansion level and proliferative capacity as well as by close interactions with activated CD4 T cells in tumors, are predictive of immunotherapy response. Our integrative resource depicts distinct clinically relevant TIB subsets, laying a foundation for further exploration of functional commonality and diversity of B cells in cancer.

Neutrophil profiling illuminates anti-tumor antigen-presenting potency.

Neutrophils, the most abundant and efficient defenders against pathogens, exert opposing functions across cancer types. However, given their short half-life, it remains challenging to explore how neutrophils adopt specific fates in cancer. Here, we generated and integrated single-cell neutrophil transcriptomes from 17 cancer types (225 samples from 143 patients). Neutrophils exhibited extraordinary complexity, with 10 distinct states including inflammation, angiogenesis, and antigen presentation. Notably, the antigen-presenting program was associated with favorable survival in most cancers and could be evoked by leucine metabolism and subsequent histone H3K27ac modification. These neutrophils could further invoke both (neo)antigen-specific and antigen-independent T cell responses. Neutrophil delivery or a leucine diet fine-tuned the immune balance to enhance anti-PD-1 therapy in various murine cancer models. In summary, these data not only indicate the neutrophil divergence across cancers but also suggest therapeutic opportunities such as antigen-presenting neutrophil delivery.

Isthmus progenitor cells contribute to homeostatic cellular turnover and support regeneration following intestinal injury.

The currently accepted intestinal epithelial cell organization model proposes that Lgr5+ crypt-base columnar (CBC) cells represent the sole intestinal stem cell (ISC) compartment. However, previous studies have indicated that Lgr5+ cells are dispensable for intestinal regeneration, leading to two major hypotheses: one favoring the presence of a quiescent reserve ISC and the other calling for differentiated cell plasticity. To investigate these possibilities, we studied crypt epithelial cells in an unbiased fashion via high-resolution single-cell profiling. These studies, combined with in vivo lineage tracing, show that Lgr5 is not a specific ISC marker and that stemness potential exists beyond the crypt base and resides in the isthmus region, where undifferentiated cells participate in intestinal homeostasis and regeneration following irradiation (IR) injury. Our results provide an alternative model of intestinal epithelial cell organization, suggesting that stemness potential is not restricted to CBC cells, and neither de-differentiation nor reserve ISC are drivers of intestinal regeneration.

Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective.

Treatment failure for the lethal brain tumor glioblastoma (GBM) is attributed to intratumoral heterogeneity and tumor evolution. We utilized 3D neuronavigation during surgical resection to acquire samples representing the whole tumor mapped by 3D spatial coordinates. Integrative tissue and single-cell analysis revealed sources of genomic, epigenomic, and microenvironmental intratumoral heterogeneity and their spatial patterning. By distinguishing tumor-wide molecular features from those with regional specificity, we inferred GBM evolutionary trajectories from neurodevelopmental lineage origins and initiating events such as chromothripsis to emergence of genetic subclones and spatially restricted activation of differential tumor and microenvironmental programs in the core, periphery, and contrast-enhancing regions. Our work depicts GBM evolution and heterogeneity from a 3D whole-tumor perspective, highlights potential therapeutic targets that might circumvent heterogeneity-related failures, and establishes an interactive platform enabling 360° visualization and analysis of 3D spatial patterns for user-selected genes, programs, and other features across whole GBM tumors.

The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation.

Dexamethasone is a life-saving treatment for severe COVID-19, yet its mechanism of action is unknown, and many patients deteriorate or die despite timely treatment initiation. Here, we identify dexamethasone treatment-induced cellular and molecular changes associated with improved survival in COVID-19 patients. We observed a reversal of transcriptional hallmark signatures in monocytes associated with severe COVID-19 and the induction of a monocyte substate characterized by the expression of glucocorticoid-response genes. These molecular responses to dexamethasone were detected in circulating and pulmonary monocytes, and they were directly linked to survival. Monocyte single-cell RNA sequencing (scRNA-seq)-derived signatures were enriched in whole blood transcriptomes of patients with fatal outcome in two independent cohorts, highlighting the potential for identifying non-responders refractory to dexamethasone. Our findings link the effects of dexamethasone to specific immunomodulation and reversal of monocyte dysregulation, and they highlight the potential of single-cell omics for monitoring in vivo target engagement of immunomodulatory drugs and for patient stratification for precision medicine approaches.

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle.

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.

Automated profiling of gene function during embryonic development.

Systematic functional profiling of the gene set that directs embryonic development is an important challenge. To tackle this challenge, we used 4D imaging of C. elegans embryogenesis to capture the effects of 500 gene knockdowns and developed an automated approach to compare developmental phenotypes. The automated approach quantifies features-including germ layer cell numbers, tissue position, and tissue shape-to generate temporal curves whose parameterization yields numerical phenotypic signatures. In conjunction with a new similarity metric that operates across phenotypic space, these signatures enabled the generation of ranked lists of genes predicted to have similar functions, accessible in the PhenoBank web portal, for ∼25% of essential development genes. The approach identified new gene and pathway relationships in cell fate specification and morphogenesis and highlighted the utilization of specialized energy generation pathways during embryogenesis. Collectively, the effort establishes the foundation for comprehensive analysis of the gene set that builds a multicellular organism.

Multimodal charting of molecular and functional cell states via in situ electro-sequencing.

Paired mapping of single-cell gene expression and electrophysiology is essential to understand gene-to-function relationships in electrogenic tissues. Here, we developed in situ electro-sequencing (electro-seq) that combines flexible bioelectronics with in situ RNA sequencing to stably map millisecond-timescale electrical activity and profile single-cell gene expression from the same cells across intact biological networks, including cardiac and neural patches. When applied to human-induced pluripotent stem-cell-derived cardiomyocyte patches, in situ electro-seq enabled multimodal in situ analysis of cardiomyocyte electrophysiology and gene expression at the cellular level, jointly defining cell states and developmental trajectories. Using machine-learning-based cross-modal analysis, in situ electro-seq identified gene-to-electrophysiology relationships throughout cardiomyocyte development and accurately reconstructed the evolution of gene expression profiles based on long-term stable electrical measurements. In situ electro-seq could be applicable to create spatiotemporal multimodal maps in electrogenic tissues, potentiating the discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo.

Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson's disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.

Mapping the transcriptome: Realizing the full potential of spatial data analysis.

RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.

Early Alzheimer's disease pathology in human cortex involves transient cell states.

Cellular perturbations underlying Alzheimer's disease (AD) are primarily studied in human postmortem samples and model organisms. Here, we generated a single-nucleus atlas from a rare cohort of cortical biopsies from living individuals with varying degrees of AD pathology. We next performed a systematic cross-disease and cross-species integrative analysis to identify a set of cell states that are specific to early AD pathology. These changes-which we refer to as the early cortical amyloid response-were prominent in neurons, wherein we identified a transitional hyperactive state preceding the loss of excitatory neurons, which we confirmed by acute slice physiology on independent biopsy specimens. Microglia overexpressing neuroinflammatory-related processes also expanded as AD pathology increased. Finally, both oligodendrocytes and pyramidal neurons upregulated genes associated with ß-amyloid production and processing during this early hyperactive phase. Our integrative analysis provides an organizing framework for targeting circuit dysfunction, neuroinflammation, and amyloid production early in AD pathogenesis.

An oncogenic phenoscape of colonic stem cell polarization.

Cancer cells are regulated by oncogenic mutations and microenvironmental signals, yet these processes are often studied separately. To functionally map how cell-intrinsic and cell-extrinsic cues co-regulate cell fate, we performed a systematic single-cell analysis of 1,107 colonic organoid cultures regulated by (1) colorectal cancer (CRC) oncogenic mutations, (2) microenvironmental fibroblasts and macrophages, (3) stromal ligands, and (4) signaling inhibitors. Multiplexed single-cell analysis revealed a stepwise epithelial differentiation phenoscape dictated by combinations of oncogenes and stromal ligands, spanning from fibroblast-induced Clusterin (CLU)+ revival colonic stem cells (revCSCs) to oncogene-driven LRIG1+ hyper-proliferative CSCs (proCSCs). The transition from revCSCs to proCSCs is regulated by decreasing WNT3A and TGF-ß-driven YAP signaling and increasing KRASG12D or stromal EGF/Epiregulin-activated MAPK/PI3K flux. We find that APC loss and KRASG12D collaboratively limit access to revCSCs and disrupt stromal-epithelial communication-trapping epithelia in the proCSC fate. These results reveal that oncogenic mutations dominate homeostatic differentiation by obstructing cell-extrinsic regulation of cell-fate plasticity.

Limb development genes underlie variation in human fingerprint patterns.

Fingerprints are of long-standing practical and cultural interest, but little is known about the mechanisms that underlie their variation. Using genome-wide scans in Han Chinese cohorts, we identified 18 loci associated with fingerprint type across the digits, including a genetic basis for the long-recognized "pattern-block" correlations among the middle three digits. In particular, we identified a variant near EVI1 that alters regulatory activity and established a role for EVI1 in dermatoglyph patterning in mice. Dynamic EVI1 expression during human development supports its role in shaping the limbs and digits, rather than influencing skin patterning directly. Trans-ethnic meta-analysis identified 43 fingerprint-associated loci, with nearby genes being strongly enriched for general limb development pathways. We also found that fingerprint patterns were genetically correlated with hand proportions. Taken together, these findings support the key role of limb development genes in influencing the outcome of fingerprint patterning.

Discovering dominant tumor immune archetypes in a pan-cancer census.

Cancers display significant heterogeneity with respect to tissue of origin, driver mutations, and other features of the surrounding tissue. It is likely that individual tumors engage common patterns of the immune system-here "archetypes"-creating prototypical non-destructive tumor immune microenvironments (TMEs) and modulating tumor-targeting. To discover the dominant immune system archetypes, the University of California, San Francisco (UCSF) Immunoprofiler Initiative (IPI) processed 364 individual tumors across 12 cancer types using standardized protocols. Computational clustering of flow cytometry and transcriptomic data obtained from cell sub-compartments uncovered dominant patterns of immune composition across cancers. These archetypes were profound insofar as they also differentiated tumors based upon unique immune and tumor gene-expression patterns. They also partitioned well-established classifications of tumor biology. The IPI resource provides a template for understanding cancer immunity as a collection of dominant patterns of immune organization and provides a rational path forward to learn how to modulate these to improve therapy.

Mapping transcriptomic vector fields of single cells.

Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo's power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.