Maiia Shulman

Abstract Constructing joint representations from multimodal single-cell datasets is crucial for understanding cellular heterogeneity and function. Traditional methods, such as factor analysis and kNN-based approaches, face computational limitations with scalability across large datasets and multiple modalities. In this work, we demonstrate the product-of-experts VAE-based model, which offers a flexible, scalable solution for integrating multimodal data, allowing for the seamless mapping of both unimodal and multimodal queries onto a reference atlas. We evaluate how different strategies for combining modalities in the VAE framework impact query-to-reference mapping across diverse datasets, including CITE-seq and spatial metabolomics. Our benchmarks assess batch effect correction, biological signal preservation, and imputation of missing modalities. We showcase our approach in a mosaic setting, integrating CITE-seq and multiome data to accurately map unimodal and multimodal queries into the joint latent space. We extend this to spatial data by integrating gene expression and metabolomics from paired Visium and MALDI-MSI slides, achieving a high correlation in metabolite predictions from spatial gene expression. Our results demonstrate that this VAE-based framework is scalable, robust, and easily applicable across multiple modalities, providing a powerful tool for data imputation, querying, and biological discovery.

Abstract Optogenetics uses light‐sensitive proteins, so‐called optogenetic tools, for highly precise spatiotemporal control of cellular states and signals. The major limitations of such tools include the overlap of excitation spectra, phototoxicity, and lack of sensitivity. The protein characterized in this study, the Japanese lamprey parapinopsin, which we named UVLamP, is a promising optogenetic tool to overcome these limitations. Using a hybrid strategy combining molecular, cellular, electrophysiological, and computational methods we elucidated a structural model of the dark state and probed the optogenetic potential of UVLamP. Interestingly, it is the first described bistable vertebrate opsin that has a charged amino acid interacting with the Schiff base in the dark state, that has no relevance for its photoreaction. UVLamP is a bistable UV‐sensitive opsin that allows for precise and sustained optogenetic control of G protein‐coupled receptor (GPCR) pathways and can be switched on, but more importantly also off within milliseconds via lowintensity short light pulses. UVLamP exhibits an extremely narrow excitation spectrum in the UV range allowing for sustained activation of the G i/o pathway with a millisecond UV light pulse. Its sustained pathway activation can be switched off, surprisingly also with a millisecond blue light pulse, minimizing phototoxicity. Thus, UVLamP serves as a minimally invasive, narrow‐bandwidth probe for controlling the G i/o pathway, allowing for combinatorial use with multiple optogenetic tools or sensors. Because UVLamP activated G i/o signals are generally inhibitory and decrease cellular activity, it has tremendous potential for health‐related applications such as relieving pain, blocking seizures, and delaying neurodegeneration.

Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing the spatial signals present in a tissue. This requires selecting the most informative, yet minimal, set of genes to profile (gene set selection) for which it is possible to build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or new states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both gene set specificity for cell type identification and within-cell type expression variation to resolve spatially distinct populations while considering prior knowledge as well as probe design and expression constraints. We evaluated Spapros and show that it outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a single-cell resolution in situ hybridization on tissues (SCRINSHOT) experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types.

Abstract Cytokines orchestrate immune responses, yet we still lack a comprehensive understanding of their specific effects across human immune cells due to their pleiotropy, context dependence and extensive functional redundancy. Here, we present a Human Cytokine Dictionary, created from high-resolution single-cell transcriptomes of 9,697,974 human peripheral blood mononuclear cells (PBMC) from 12 donors stimulated in vitro with 90 different cytokines. We describe donor-specific response variation and uncover robust consensus cytokine signatures across individuals. We then delineate similarities between cytokine response profiles, and derive cytokine-induced immune programs that organize responsive genes into data-driven, biologically interpretable functional modules. By integrating cell type-specific responses with expression of cytokines, we infer higher-order cell-to-cell and cytokine-to-cytokine communication networks exemplified by an IL-32-β-initiated signaling cascade, which rewires myeloid programs by inducing neutrophil-recruiting factors while suppressing Th1-responses and promoting IL-10-family cytokines. Finally, we show how the Human Cytokine Dictionary enables the interpretation of cytokine-driven immune responses in other studies and disease contexts, including systemic lupus erythematosus, multiple sclerosis, and non-small cell lung carcinoma. Together, the Human Cytokine Dictionary constitutes the first comprehensive cell type-resolved transcriptional screen of human cytokine responses and provides an essential open-access, easy-to-use community resource with accompanying software package to advance our understanding of cytokine biology in human disease and guide therapeutic discovery.