The advent of single-cell genomics has brought about new efforts to characterize and catalog all of the cell types in the human body. Despite these efforts, the very definition of a “cell type” is under debate. In this post, I will discuss a conceptual framework for defining cell types as subsets of states in an underlying cellular state space. Moreover, I will link the cellular state space to biomedical ontologies that attempt to capture biological knowledge regarding cell types.
The evolving concept of cell identity in the single cell era
Summary: This Spotlight explores emerging technologies that are enabling the systematic and unbiased quantification of cell identity, and how these efforts will enable the construction of high-resolution, dynamic cell atlases.
A periodic table of cell types
Summary: This Hypothesis proposes a periodic table that aligns cell types according to their developmental stages, connecting them to one another according to the universal axis from stem cells to differentiated cells.
Organism-scale annotation with Pan-human Azimuth
Single-cell atlases now span many human tissues, but inconsistent annotations across studies limit their utility as a unified reference. We introduce Pan-human Azimuth, a supervised neural network that maps human cells from diverse tissues and datasets onto a single hierarchical organism-scale typology.
Dynamic Landscape Analysis of cell fate decisions provides predictive models of neural development from single-cell data
Building a mechanistic understanding of cell fate decisions remains a fundamental goal of developmental biology, with implications for stem cell therapies, regenerative medicine and understanding disease mechanisms. Single-cell transcriptomics provides a detailed picture of the cellular states observed during these decisions, but building dynamic and predictive models from these data remains a challenge. Here, we present dynamic landscape analysis (DLA), an integrative framework that applies dynamical systems theory to identify stable cell states, map transition pathways, and generate a predictive cell fate decision landscape from single-cell data. Applying this framework to vertebrate neural tube development revealed that progenitor specification by Sonic Hedgehog (Shh) can be captured in a landscape with an unexpected topology in which initially divergent lineages converge to the same fate through multiple distinct routes. The model accurately predicted cellular responses and cell fate allocation for unseen dynamic signalling regimes. Cross-species validation using human embryonic organoid data demonstrated conservation of this decision-making architecture. By modelling the dynamic responses that drive cell fate decisions, the DLA framework provides a quantitative and generative framework for extracting mechanistic insights from high-dimensional single-cell data.
Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon
Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon’s efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.
Single-Nucleus Multi-Omic Atlas Maps Regulatory Architecture and Non-Coding Variant Effects across Adult Human Tissues
Diverse human cell types establish specialized functions through lineage- and context-specific regulatory programs. Interpreting non-coding genetic risk requires integrated multi-omic reference maps that directly connect regulatory DNA to cellular expression across human tissues.
Embryo-scale reverse genetics at single-cell resolution
The maturation of single-cell transcriptomic technologies has facilitated the generation of comprehensive cellular atlases from whole embryos1–4. A majority of these data, however, has been collected from wild-type embryos without an appreciation for the latent variation that is present in development. Here we present the ‘zebrafish single-cell atlas of perturbed embryos’: single-cell transcriptomic data from 1,812 individually resolved developing zebrafish embryos, encompassing 19 timepoints, 23 genetic perturbations and a total of 3.2 million cells. The high degree of replication in our study (eight or more embryos per condition) enables us to estimate the variance in cell type abundance organism-wide and to detect perturbation-dependent deviance in cell type composition relative to wild-type embryos. Our approach is sensitive to rare cell types, resolving developmental trajectories and genetic dependencies in the cranial ganglia neurons, a cell population that comprises less than 1% of the embryo. Additionally, time-series profiling of individual mutants identified a group of brachyury-independent cells with strikingly similar transcriptomes to notochord sheath cells, leading to new hypotheses about early origins of the skull. We anticipate that standardized collection of high-resolution, organism-scale single-cell data from large numbers of individual embryos will enable mapping of the genetic dependencies of zebrafish cell types, while also addressing longstanding challenges in developmental genetics, including the cellular and transcriptional plasticity underlying phenotypic diversity across individuals.
Comparing phenotypic manifolds with Kompot: Detecting differential abundance and gene expression at single-cell resolution
Single-cell studies are frequently designed to compare across conditions such as health and disease. However, existing computational approaches typically rely on grouping cells into discrete populations before making comparisons, which can limit resolution for detecting state-dependent changes. Here, we introduce Kompot, a statistical framework for comparative analysis of multi-condition single-cell data. Kompot quantifies both differential abundance, capturing how cells redistribute across the phenotypic space, and differential expression, identifying condition-specific transcriptional changes that may be localized, heterogeneous, or oppositely regulated across states. By modeling cell density and gene expression as continuous functions over a shared cell-state representation, Kompot enables single-cell–resolution inference with principled uncertainty estimates, without requiring predefined clusters or cell types. Applying Kompot to aging murine bone marrow, we identified a continuum of shifts in hematopoietic stem cell and mature cell states, transcriptional remodeling of monocytes independent of compositional changes, and divergent regulation of oxidative stress response genes across cell types. We demonstrate the utility of Kompot in disease settings by identifying cell-state and gene expression changes associated with improved efficacy of combinatorial immunotherapy in melanoma. Additionally, Kompot enables multi-sample comparative analysis by accounting for sample-to-sample heterogeneity. By capturing both global and cell-state–specific effects of perturbation, the Kompot framework is broadly applicable to dissecting condition-specific effects in complex single-cell landscapes. ### Competing Interest Statement The authors have declared no competing interest. National Institutes of Health, R35GM147125, R01CA292932, T32GM136534, S10OD028685 The Mark Foundation for Cancer Research, https://ror.org/00v7th354, Endeavor Award Edward P. Evans Foundation, https://ror.org/03h22gm35, Discovery Research Grant Brotman Baty Institute, https://ror.org/03jxvbk42, Pilot Award
Hierarchical classification of immune cell transcriptomes at population-scale
Accurate immune cell classification is essential for interpreting single-cell RNA sequencing (scRNA-seq) data. However, progress is constrained by the lack of independent, high-resolution benchmarks, as the routine integration of datasets introduces statistical dependencies that artificially inflate model generalizability. Here, we present the single-cell universal classification omnibus (Suco), a resource of independent, uniform expert annotations, and Compocyte, a modular hierarchical classifier. Together, they establish a framework designed for the scale of human population immunology. This approach substantially outperforms existing classifiers while facilitating expert review of ambiguous annotations. Applying Compocyte across 50 studies, including three newly generated datasets, we classified 15.6 million leukocytes from 3,965 patients. Within this expansive cohort, we identified a new tumor-associated resorptive macrophage phenotype, a non-canonical monocyte subtype in subclinical cytokine release syndrome, and the programmatic erosion of T cell memory stemness across metastatic sites. Suco and Compocyte thus provide a generalizable architecture and benchmark capable of sustaining high-resolution annotation across massive clinical cohorts. ### Competing Interest Statement CMR has consulted regarding oncology drug development with Amgen, AstraZeneca, Daiichi Sankyo, Genentech, Merck, and Novartis, and has received licensing and royalty payments for DLL3-directed therapeutics. T.W. reports stock ownership for Roche, Astra Zeneca, Bayer, Innate Pharma, Kyntra, Illumina, 10x Genomics, and Merck KGaA as well as research funding from Atrandi Biosciences, Vilnius, Lithuania; CanVirex AG, Basel Switzerland; and Institut fuer Klinische Krebsforschung GmbH, Frankfurt, Germany, and travel funding from Roche, Basel, Switzerland. S.Z. reports advisory board membership and honoraria from Amgen, Astellas, AstraZeneca, Bayer, Bristol-Myers Squibb, Daiichi Sankyo, Eisai, EUSA, Gilead, Ipsen, Johnson&Johnson, Lilly, MedSir, Medtoday, Merck, MSD, Novartis, Pfizer, Roche, Sanofi Aventis, StreamedUp, Urotrials, Urotube, Zentiva and resarch funding from Eisai. S.Z. reports clinical trial support from Amgen, AstraZeneca, AVEO, Bayer, Biontech, Bristol-Myers Squibb, Calithera, Exelixis, Gilead, Lilly, MSD, Novartis, Pfizer, Roche, Seagen/Astellas, Urotrials and travels & conference support from Amgen, Astellas, AstraZeneca, Bayer, EISAI, Ipsen, Johnson&Johnson, Merck, MSD, Pfizer. All remaining authors declare no relevant competing interests. Spanish Association Against Cancer, PI049999 Federal Ministry of Research, Technology and Space, 001001KT2322 National Cancer Institute, R35 CA263816 National Cancer Institute, U24 CA213274 National Cancer Institute, P30 CA008748 Research Council of Lithuania, P-MIP-24-93
Senomic view of the cell: Senome <i>versus</i> Genome
In the legacy of Thomas Henry Huxley, and his ‘epigenetic’ philosophy of biology, cells are proposed to represent a trinity of three memory-storing media: Senome, Epigenome, and Genome that togethe...
Rectangle: robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data
Bulk RNA-seq enables effective profiling of large cohorts and complex experimental designs, but current single-cell-informed deconvolution methods incompletely resolve closely related cell phenotypes, do not scale efficiently to large single-cell datasets, or fail to account for cellular content not represented in the reference. Here, we present Rectangle, an scverse Python framework for single-cell-informed deconvolution of bulk RNA-seq data.
Spatial Transcriptomics As Rasterized Image Tensors (STARIT) characterizes cell states with subcellular molecular heterogeneity
Imaging-based spatially resolved transcriptomics (imSRT) technologies provide high-throughput molecular-resolution spatial characterization of genes within cells. Conventional analysis methods to identify cell-types and states in imSRT data rely on gene count matrices derived from tallying the number of mRNA molecules detected for each gene per segmented cell, thereby overlooking subcellular heterogeneity that can be useful in defining cell states. To take advantage of the molecular-resolution information in imSRT data and potentially identify cell-states based on subcellular heterogeneity, we developed STARIT (Spatial Transcriptomics As Rasterized Image Tensors). STARIT converts transcripts within segmented cells in imSRT data into an image-based tensor representation that can be combined with deep learning computer vision models for downstream analysis. Using simulated and real imSRT data, we demonstrate that STARIT distinguishes transcriptionally distinct cell-types and further separates cell states based on subcellular transcript localization, which conventional gene count analysis fails to capture. By providing a standardized framework to encode subcellular molecular information in imSRT data, STARIT will enable deeper insights into subcellular heterogeneity and enhance the identification and characterization of cell-types and states that are overlooked by gene count representations.
VAPOR: A variational autoencoder with transport operators to disentangle cellular gene expression dynamics of co-occurring biological processes in time and space
Abstract Single-cell and spatial transcriptomics enable the analysis of cellular states and dynamics in gene expression, revealing how diverse biological processes relate to these states over time and space. To study these dynamics, trajectory inference methods order cells along computationally inferred paths to reconstruct gradual transitions in cell states. However, by encouraging smooth and continuous trajectories, these approaches tend to conflate co-occurring processes-such as proliferation, maturation, and spatial organization-that are jointly reflected in gene expression, potentially overlooking process-specific gene expression dynamics. To address this, we developed VAPOR, which integrates a variational autoencoder with transport operators to model and disentangle cellular gene expression dynamics for potentially co-occurring biological processes. VAPOR inputs single-cell (or spatial) gene expression data into a variational autoencoder (VAE) to learn the latent states of cells and then models their latent dynamics as an ordinary differential equation. The latent dynamics are further decomposed into process-specific components parameterized by transport operators (TOs) and their corresponding process weights. Each TO defines a process-specific dynamics, and its weight for each cell quantifies the process's contribution to the cell dynamics. After assessment by simulation studies, we applied VAPOR with benchmarking to real data, including time-course scRNA-seq from postconceptual human brain development, spatial transcriptomics of the mouse hippocampus, and cross-species scRNA-seq spanning human and macaque first-trimester forebrain development. In these applications, VAPOR has identified a variety of temporal and spatial co-occurring processes, such as cell cycle, gliogenesis, neurogenesis, and neuronal migration, along with associated dynamic genes, including those species-specific to human and macaque development. VAPOR is available as an open-source tool for general-purpose use. ### Competing Interest Statement The authors have declared no competing interest.
How to understand cells, tissues and organisms as agents with agendas | Aeon Essays
Biology’s next great horizon is to understand cells, tissues and organisms as agents with agendas (even if unthinking ones)
A multimodal perturbation atlas defines the phenotypic resolution of cellular morphology
Because cells are complex dynamical systems, modeling cellular behaviors requires methods that capture how cells evolve across time, environments, and interventions. Microscopy is uniquely suited to this goal in that it can be applied to living cells in their native context. However, the phenotypic resolving power of live-cell microscopy remains incompletely characterized, particularly relative to molecular assays. Here, we present a multimodal perturbation atlas of 1,000 pooled CRISPR knockouts in A549 cells, profiled by fluorescence microscopy (39 live, 13 fixed markers), label-free phase imaging of the same live cells, and single-cell RNA sequencing (scRNA-seq). Totaling ∼57 million single-cell profiles, our data yield rich cell-biological signatures that map individual gene function. We find that phase imaging matches — and, with sufficient cell coverage, exceeds — the phenotypic resolution of fluorescence imaging and scRNA-seq, while capturing higher-order pathway organization that scRNA-seq does not resolve. These results establish intrinsic morphology as a high-precision readout of cellular state, and lay a foundation for live-cell profiling of phenotypic trajectories. ### Competing Interest Statement The authors have declared no competing interest. Biohub, Redwoood City, CA, USA
