Leah C. Dorman

Modeling cellular behavior requires measurements that capture how cells evolve across time, environments, and interventions. Microscopy is uniquely suited to this goal: it is non-destructive and can be applied to living cells in their native context. Yet its phenotypic resolving power remains incompletely characterized relative to molecular assays. Here, we present a multimodal perturbation atlas of 1,000 pooled CRISPR knockouts in A549 cells, profiled by fluorescence microscopy (42 live, 13 fixed markers), label-free quantitative phase imaging of the same live cells (at single timepoints), and single-cell RNA sequencing (scRNA-seq). We develop deep learning frameworks to interpret the rich cell-biological signatures in these ~65M single-cell profiles. At matched reagent cost, phase imaging exceeds the phenotypic resolution of both fluorescence imaging and scRNA-seq, and more reliably recovers higher-order pathway organization. These results establish intrinsic morphology as a high-precision readout of cellular state, and lay a foundation for live-cell profiling of phenotypic trajectories.

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.

The mouse is a tractable model for human ovarian biology; however, its utility is limited by incomplete understanding of how transcription and signaling differ interspecifically and with age. We compared ovaries between species using three-dimensional imaging, single-cell transcriptomics, and functional studies. In mice, we mapped declining follicle numbers and oocyte competence during aging; in human ovaries, we identified cortical follicle pockets and decreases in density. Oocytes had species-specific gene expression patterns during growth that converged toward maturity. Age-related transcriptional changes were greater in oocytes than in granulosa cells across species, although mature oocytes change more in humans. We identified ovarian sympathetic nerves and glia; axon density increased in aged ovaries and, when ablated in mice, perturbed folliculogenesis. This comparative atlas defines shared and species-specific hallmarks of ovarian biology.

Mouse is a tractable model for human ovarian biology, however its utility is limited by incomplete understanding of how transcription and signaling differ interspecifically and with age. We compared ovaries between species using 3D-imaging, single-cell transcriptomics, and functional studies. In mice, we mapped declining follicle numbers and oocyte competence during aging; in human ovaries, we identified cortical follicle pockets and density changes. Oocytes had species-specific gene expression patterns during growth that converged toward maturity. Age-related transcriptional changes were greater in oocytes than granulosa cells across species, although mature oocytes change more in humans. We identified ovarian sympathetic nerves and glia; nerve density increased in aged human ovaries and, when ablated in mice, perturbed folliculogenesis. This comparative atlas defines shared and species-specific hallmarks of ovarian biology.

Mycobacterium tuberculosis (MTB) ESX-1, a type VII secretion system, is a key virulence determinant contributing to MTB’s survival within lung mononuclear phagocytes (MNPs), but its effect on MNP recruitment and differentiation remains unknown. Here, using multiple single-cell RNA sequencing techniques, we studied the role of ESX-1 in MNP heterogeneity and response in mice and murine bone marrow–derived macrophages (BMDM). We found that ESX-1 is required for MTB to recruit diverse MNP subsets with high MTB burden. Further, MTB induces a transcriptional signature of immune evasion in lung macrophages and BMDM in an ESX-1–dependent manner. Spatial transcriptomics revealed an up-regulation of permissive features within MTB lesions, where monocyte-derived macrophages concentrate near MTB-infected cells. Together, our findings suggest that MTB ESX-1 facilitates the recruitment and differentiation of MNPs, which MTB can infect and manipulate for survival. Our dataset across various models and methods could contribute to the broader understanding of recruited cell heterogeneity during MTB lung infection.

Traumatic brain injury (TBI) is a leading cause of mortality and disability worldwide and can lead to secondary sequelae such as increased seizure susceptibility. Emerging work suggests that the thalamus, the relay center of the brain that undergoes secondary damage after cortical TBI, is involved with heightened seizure risks after TBI. TBI also induces the recruitment of peripheral immune cells, including T cells, to the site(s) of injury, but it is unclear how these cells impact neurological sequelae post-TBI. Here, we characterize the identities and kinetics of lymphocytic infiltrates into the cortex and thalamus using a mouse model of cortical TBI. We identify a population of IFNγ-producing type 1 lymphocytes that infiltrate specific thalamic subregions over weeks following injury, where they elicit a local IFNγ response in microglia and neuronal subset(s). Depletion of CD4⁺ T cells protects mice from TBI-induced seizure susceptibility by de-repressing other non-CD4⁺ type 1 lymphocytes and disease-associated microglia (DAMs) in the thalamus. Strikingly, we find that a single dose of IFNγ prior to challenge with a proconvulsant agent was sufficient to reduce TBI-induced seizure incidence, severity, and mortality. This work identifies IFNγ as a direct modulator of TBI-associated seizure susceptibility, which could have therapeutic implications for the treatment of TBI patients.

The innate immune system shapes brain development and is implicated in neurodevelopmental diseases. It is critical to define the relevant immune cells and signals and their impact on brain circuits. In this work, we found that group 2 innate lymphoid cells (ILC2s) and their cytokine interleukin-13 (IL-13) signaled directly to inhibitory interneurons to increase inhibitory synapse density in the developing mouse brain. ILC2s expanded and produced IL-13 in the developing brain meninges. Loss of ILC2s or IL-13 signaling to interneurons decreased inhibitory, but not excitatory, cortical synapses. Conversely, ILC2s and IL-13 were sufficient to increase inhibitory synapses. Loss of this signaling pathway led to selective impairments in social interaction. These data define a type 2 neuroimmune circuit in early life that shapes inhibitory synapse development and behavior.

Single-cell RNA sequencing is revolutionizing our understanding of cell state dynamics, allowing researchers to observe the progression of individual cells’ transcriptomic profiles over time. Among the computational techniques used to predict future cellular states, RNA velocity has emerged as a predominant tool for modeling transcriptional dynamics. RNA velocity leverages the mRNA maturation process to generate velocity vectors that predict the likely future state of a cell, offering insights into cellular differentiation, aging, and disease progression. Although this technique has shown promise across biological fields, the performance accuracy varies depending on the RNA velocity method and dataset. We established a comparative pipeline and analyzed the performance of five RNA velocity methods on three datasets based on local consistency, method agreement, identification of driver genes, and robustness to sequencing depth. This benchmark provides a resource for scientists to understand the strengths and limitations of different RNA velocity methods.

Mycobacterium tuberculosis (MTB) infects and replicates in lung mononuclear phagocytes (MNPs) with astounding ability to evade elimination. ESX-1, a type VII secretion system, acts as a virulence determinant that contributes to MTB’s ability to survive within MNPs, but its effect on MNP recruitment and/or differentiation remains unknown. Here, using single-cell RNA sequencing, we studied the role of ESX-1 in MNP heterogeneity and response in mice and murine bone marrow-derived macrophages (BMDM). We found that ESX-1 is required for MTB to recruit diverse MNP subsets with high MTB burden. Further, MTB induces an anti-inflammatory signature in MNPs and BMDM in an ESX-1 dependent manner. Similarly, spatial transcriptomics revealed an upregulation of anti-inflammatory signals in MTB lesions, where monocyte-derived macrophages concentrate near MTB-infected cells. Together, our findings suggest that MTB ESX-1 mediates the recruitment and differentiation of anti-inflammatory MNPs, which MTB can infect and manipulate for survival.

Assigning cell identity to clusters of single cells is an essential step towards extracting biological insights from many genomics datasets. Although annotation workflows for datasets built with a single modality are well established, limitations exist in annotating cell types in datasets with multiple modalities due to the need for a framework to exploit them jointly. While, in principle, different modalities could convey complementary information about cell identity, it is unclear to what extent they can be combined to improve the accuracy and resolution of cell type annotations. Here, we present a conceptual framework to examine and jointly interrogate distinct modalities to identify cell types. We integrated our framework into a series of vignettes, using immune cells as a well-studied example, and demonstrate cell type annotation workflows ranging from using single-cell RNA-seq datasets alone, to using multiple modalities such as single-cell Multiome (RNA and chromatin accessibility), CITE-seq (RNA and surface proteins). In some cases, one or other single modality is superior to the other for identification of specific cell types, in others combining the two modalities improves resolution and the ability to identify finer subpopulations. Finally, we use interactive software from CZ CELLxGENE community tools to visualize and integrate histological and spatial transcriptomic data.