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Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation–response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation–response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth. scPerturb is an information resource for single-cell perturbation data analysis and comparison.

Single-cell transcriptomics has transformed biology by enabling deep interrogation of the RNA contents of individual cells. This has led to in-depth study of cellular heterogeneity and the role of cell state transitions in disease. Observational single-cell studies have moved towards hypothesis generation about complex cellular processes; interventional studies enable mechanistic insights. Here, we use single cell transcriptomics to identify cell states underlying nasal polyp formation. We also interrogate how gene expression in the sinus changes in response to asthma treatment, combining single cell and bulk analyses for a more complete view. We then move beyond changes in individual cell types to uncover how cells relate to each other, using matrix decomposition to reveal multi-cell- type changes in gene expression in breast cancer, suggesting interaction signatures specific to breast cancer subtypes, and interactions predicting response to treatment. Our findings on drug response in these two disease cases were limited by a lack of robust statistical tools; to improve tools for interrogating perturbation response in single cells, we created an annotation-harmonized collection of single cell perturbation studies, then used this data resource to characterize the performance of E- statistics for evaluating perturbation similarity and efficacy. In total, this thesis contains two stories of using perturbation in patients to study disease, and one example of using a collection of datasets to improve methods used for interrogating biological systems.

Triple-negative breast cancer (TNBC) is an aggressive malignancy with limited targeted therapies and variable responses to conventional chemotherapy, influenced by intratumoral heterogeneity and complex tumor microenvironment (TME) interactions. Understanding spatiotemporal cellular interplay and tissue organization is crucial for advancing tumor biology and improving patient stratification. Spatially resolved proteomics, such as Imaging Mass Cytometry (IMC), offers a powerful approach to dissect the TME. We present an end-to-end computational pipeline for robust quantitative analysis of large-scale IMC datasets, addressing the challenge of batch effects through image-level contrast adjustment. Applying this framework to 813 tissue regions encompassing over 4 million cells from 63 TNBC patients, we revealed distinct spatial arrangements of cell types between chemotherapy responders and non-responders. Non-responders showed reduced cytotoxic T-cell infiltration into tumor regions and increased spatial co-localization between fibroblasts and macrophages, a pattern that persisted and intensified after chemotherapy treatment. To integrate these complex spatial-molecular relationships, we used graph neural networks (GNNs) to predict treatment response from pre-treatment samples with AUROC=0.71. Interpretability analysis identified B7H4, CD11b, CD366, and FOXP3 as the most predictive protein markers, with fibroblasts, cancer cells, and CD8+ T cells being the most informative cell types. This study introduces a scalable analytical framework for spatial proteomics with interpretable predictions, suggesting features of tissue state that could guide treatment decisions in TNBC and further our understanding of the spatial determinants of therapeutic response.

In single-cell data workflows and modeling, distance metrics are commonly used in loss functions, model evaluation, and subpopulation analysis. However, these metrics behave differently depending on the source of variation, conditions and subpopulations in single-cell expression profiles due to data sparsity and high dimensionality. Thus, the metrics used for downstream tasks in this domain should be carefully selected. We establish a set of benchmarks with three evaluation measures, capturing desirable facets of absolute and relative distance behavior. Based on seven datasets using perturbation as ground truth, we evaluated 16 distance metrics applied to scRNA-seq data and demonstrated their application to three use cases. We find that linear metrics such as mean squared error (MSE) performed best across our three evaluation criteria. Therefore, we recommend the use of MSE for comparing single-cell RNA-seq populations and evaluating gene expression prediction models.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/theislab/perturbation-metrics

Recent biotechnological advances led to growing numbers of single-cell perturbation studies, which reveal molecular and phenotypic responses to large numbers of perturbations. However, analysis across diverse datasets is typically hampered by differences in format, naming conventions, and data filtering. In order to facilitate development and benchmarking of computational methods in systems biology, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform pre-processing and quality control pipelines and harmonize feature annotations. The resulting information resource enables efficient development and testing of computational analysis methods, and facilitates direct comparison and integration across datasets. In addition, we introduce E-statistics for perturbation effect quantification and significance testing, and demonstrate E-distance as a general distance measure for single cell data. Using these datasets, we illustrate the application of E-statistics for quantifying perturbation similarity and efficacy. The data and a package for computing E-statistics is publicly available at scperturb.org. This work provides an information resource and guide for researchers working with single-cell perturbation data, highlights conceptual considerations for new experiments, and makes concrete recommendations for optimal cell counts and read depth.

The NLRP3 inflammasome is a multi-molecular protein complex that converts inactive cytokine precursors into active forms of IL-1β and IL-18. The NLRP3 inflammasome is frequently associated with the damaging inflammation of non-communicable disease states and is considered an attractive therapeutic target. However, there is much regarding the mechanism of NLRP3 activation that remains unknown. Chloride efflux is suggested as an important step in NLRP3 activation, but which chloride channels are involved is still unknown. We used chemical, biochemical, and genetic approaches to establish the importance of chloride channels in the regulation of NLRP3 in murine macrophages. Specifically, we identify LRRC8A, an essential component of volume-regulated anion channels (VRAC), as a vital regulator of hypotonicity-induced, but not DAMP-induced, NLRP3 inflammasome activation. Although LRRC8A was dispensable for canonical DAMP-dependent NLRP3 activation, this was still sensitive to chloride channel inhibitors, suggesting there are additional and specific chloride sensing and regulating mechanisms controlling NLRP3. Inflammation is a critical part of a healthy immune system, which protects us against harmful pathogens (such as bacteria or viruses) and works to restore damaged tissues. In the immune cells of our body, the inflammatory process can be activated through a group of inflammatory proteins that together are known as the NLRP3 inflammasome complex. While inflammation is a powerful mechanism that protects the human body, persistent or uncontrolled inflammation can cause serious, long-term damage. The inappropriate activation of the NLRP3 inflammasome has been implicated in several diseases, including Alzheimer’s disease, heart disease, and diabetes. The NLRP3 inflammasome can be activated by different stimuli, including changes in cell volume and exposure to either molecules produced by damaged cells or toxins from bacteria. However, the precise mechanism through which the NLRP3 becomes activated in response to these stimuli was not clear. The exit of chloride ions from immune cells is known to activate the NLRP3 inflammasome. Chloride ions exit the cell through proteins called anion channels, including volume-regulated anion channels (VRACs), which respond to changes in cell volume. Green et al. have found that, in immune cells from mice grown in the lab called macrophages, VRACs are the only chloride channels involved in activating the NLRP3 inflammasome when the cell’s volume changes. However, when the macrophages are exposed to molecules produced by damaged cells or toxins from bacteria, Green et al. discovered that other previously unidentified chloride channels are involved in activating the NLRP3 inflammasome. These results suggest that it might be possible to develop drugs to prevent the activation of the NLRP3 inflammasome that selectively target specific sets of chloride channels depending on which stimuli are causing the inflammation. Such a selective approach would minimise the side effects associated with drugs that generically suppress all NLRP3 activity by directly binding to NLRP3 itself. Ultimately, this may help guide the development of new, targeted anti-inflammatory drugs that can help treat the symptoms of a variety of diseases in humans.

Ageing is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death 1 . Despite rapid advances over recent years, many of the molecular and cellular processes that underlie the progressive loss of healthy physiology are poorly understood 2 . To gain a better insight into these processes, here we generate a single-cell transcriptomic atlas across the lifespan of Mus musculus that includes data from 23 tissues and organs. We found cell-specific changes occurring across multiple cell types and organs, as well as age-related changes in the cellular composition of different organs. Using single-cell transcriptomic data, we assessed cell-type-specific manifestations of different hallmarks of ageing—such as senescence 3 , genomic instability 4 and changes in the immune system 2 . This transcriptomic atlas—which we denote Tabula Muris Senis , or ‘Mouse Ageing Cell Atlas’—provides molecular information about how the most important hallmarks of ageing are reflected in a broad range of tissues and cell types. A single-cell transcriptomic atlas across the lifespan of the mouse, denoted Tabula Muris Senis , provides molecular information about the hallmarks of ageing in a range of tissues and cell types.

Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3′-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology. A ‘mouse atlas’, comprising single-cell transcriptomic data from more than 100,000 cells from 20 organs and tissues, has been created as a resource for cell biology.

Ageing is the single greatest cause of disease and death worldwide, and understanding the associated processes could vastly improve quality of life. Although major categories of ageing damage have been identified—such as altered intercellular communication, loss of proteostasis and eroded mitochondrial function 1 —these deleterious processes interact with extraordinary complexity within and between organs, and a comprehensive, whole-organism analysis of ageing dynamics has been lacking. Here we performed bulk RNA sequencing of 17 organs and plasma proteomics at 10 ages across the lifespan of Mus musculus , and integrated these findings with data from the accompanying Tabula Muris Senis 2 —or ‘Mouse Ageing Cell Atlas’—which follows on from the original Tabula Muris 3 . We reveal linear and nonlinear shifts in gene expression during ageing, with the associated genes clustered in consistent trajectory groups with coherent biological functions—including extracellular matrix regulation, unfolded protein binding, mitochondrial function, and inflammatory and immune response. Notably, these gene sets show similar expression across tissues, differing only in the amplitude and the age of onset of expression. Widespread activation of immune cells is especially pronounced, and is first detectable in white adipose depots during middle age. Single-cell RNA sequencing confirms the accumulation of T cells and B cells in adipose tissue—including plasma cells that express immunoglobulin J—which also accrue concurrently across diverse organs. Finally, we show how gene expression shifts in distinct tissues are highly correlated with corresponding protein levels in plasma, thus potentially contributing to the ageing of the systemic circulation. Together, these data demonstrate a similar yet asynchronous inter- and intra-organ progression of ageing, providing a foundation from which to track systemic sources of declining health at old age. Bulk RNA sequencing of organs and plasma proteomics at different ages across the mouse lifespan is integrated with data from the Tabula Muris Senis , a transcriptomic atlas of ageing mouse tissues, to describe organ-specific changes in gene expression during ageing.

The ability to slow or reverse biological ageing would have major implications for mitigating disease risk and maintaining vitality 1 . Although an increasing number of interventions show promise for rejuvenation 2 , their effectiveness on disparate cell types across the body and the molecular pathways susceptible to rejuvenation remain largely unexplored. Here we performed single-cell RNA sequencing on 20 organs to reveal cell-type-specific responses to young and aged blood in heterochronic parabiosis. Adipose mesenchymal stromal cells, haematopoietic stem cells and hepatocytes are among those cell types that are especially responsive. On the pathway level, young blood invokes new gene sets in addition to reversing established ageing patterns, with the global rescue of genes encoding electron transport chain subunits pinpointing a prominent role of mitochondrial function in parabiosis-mediated rejuvenation. We observed an almost universal loss of gene expression with age that is largely mimicked by parabiosis: aged blood reduces global gene expression, and young blood restores it in select cell types. Together, these data lay the groundwork for a systemic understanding of the interplay between blood-borne factors and cellular integrity. A transcriptomics study demonstrates cell-type-specific responses to differentially aged blood and shows young blood to have restorative and rejuvenating effects that may be invoked through enhanced mitochondrial function.