TissueMosaic: Self-supervised learning of tissue representations enables differential spatial transcriptomics across samples.

Spatial transcriptomics allows for the measurement of gene expression within the native tissue context. However, despite technological advancements, computational methods to link cell states with their microenvironment and compare these relationships across samples and conditions remain limited. To address this, we introduce Tissue Motif-Based Spatial Inference across Conditions (TissueMosaic), a self-supervised convolutional neural network designed to discover and represent tissue architectural motifs from multi-sample spatial transcriptomic datasets.

A clonally expanded nodal T-cell population diagnosed as T-cell lymphoma after CAR-T therapy.

Reports of secondary malignancies after chimeric antigen receptor (CAR)-T and possible CAR-T derived malignant transformation necessitate caution. Here we describe a patient with diffuse large B-cell lymphoma who developed new lymphadenopathy 2.5 years after CAR-T in the context of COVID-19 infection with histopathologic features consistent with T-cell lymphoma (TCL).

Prediction of protein subcellular localization in single cells.

The subcellular localization of a protein is important for its function, and its mislocalization is linked to numerous diseases. Existing datasets capture limited pairs of proteins and cell lines, and existing protein localization prediction models either miss cell-type specificity or cannot generalize to unseen proteins. Here we present a method for Prediction of Unseen Proteins' Subcellular localization (PUPS).

Scalable spatial transcriptomics through computational array reconstruction.

Spatial transcriptomics enables gene expression mapping within tissues but is often limited by imaging constraints. We present an imaging-free approach that reconstructs spatial barcode locations using molecular diffusion and dimensionality reduction. Validated against ground truth imaging, our method achieves high fidelity and scales to centimeter-sized tissues.

Evolutionary fingerprints of epithelial-to-mesenchymal transition.

Mesenchymal plasticity has been extensively described in advanced epithelial cancers; however, its functional role in malignant progression is controversial. The function of epithelial-to-mesenchymal transition (EMT) and cell plasticity in tumour heterogeneity and clonal evolution is poorly understood. Here we clarify the contribution of EMT to malignant progression in pancreatic cancer.

Spatial multiomic landscape of the human placenta at molecular resolution.

Successful pregnancy relies directly on the placenta's complex, dynamic, gene-regulatory networks. Disruption of this vast collection of intercellular and intracellular programs leads to pregnancy complications and developmental defects. In the present study, we generated a comprehensive, spatially resolved, multimodal cell census elucidating the molecular architecture of the first trimester human placenta.

Slide-tags enables single-nucleus barcoding for multimodal spatial genomics.

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions.

The molecular cytoarchitecture of the adult mouse brain.

The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types and their positions within individual anatomical structures remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA sequencing with Slide-seq-a recently developed spatial transcriptomics method with near-cellular resolution-across the entire mouse brain.

Photoselective sequencing: microscopically guided genomic measurements with subcellular resolution.

In biological systems, spatial organization and function are interconnected. Here we present photoselective sequencing, a new method for genomic and epigenomic profiling within morphologically distinct regions. Starting with an intact biological specimen, photoselective sequencing uses targeted illumination to selectively unblock a photocaged fragment library, restricting the sequencing-based readout to microscopically identified spatial regions.

Programmable eukaryotic protein synthesis with RNA sensors by harnessing ADAR.

Programmable approaches to sense and respond to the presence of specific RNAs in biological systems have broad applications in research, diagnostics, and therapeutics. Here we engineer a programmable RNA-sensing technology, reprogrammable ADAR sensors (RADARS), which harnesses RNA editing by adenosine deaminases acting on RNA (ADAR) to gate translation of a cargo protein by the presence of endogenous RNA transcripts. Introduction of a stop codon in a guide upstream of the cargo makes translation contingent on binding of an endogenous transcript to the guide, leading to ADAR editing of the stop codon and allowing translational readthrough.

Spatial maps of T cell receptors and transcriptomes reveal distinct immune niches and interactions in the adaptive immune response.

T cells mediate antigen-specific immune responses to disease through the specificity and diversity of their clonotypic T cell receptors (TCRs). Determining the spatial distributions of T cell clonotypes in tissues is essential to understanding T cell behavior, but spatial sequencing methods remain unable to profile the TCR repertoire. Here, we developed Slide-TCR-seq, a 10-μm-resolution method, to sequence whole transcriptomes and TCRs within intact tissues.

The expanding vistas of spatial transcriptomics.

The formation and maintenance of tissue integrity requires complex, coordinated activities by thousands of genes and their encoded products. Until recently, transcript levels could only be quantified for a few genes in tissues, but advances in DNA sequencing, oligonucleotide synthesis and fluorescence microscopy have enabled the invention of a suite of spatial transcriptomics technologies capable of measuring the expression of many, or all, genes in situ. These technologies have evolved rapidly in sensitivity, multiplexing and throughput.

Cell type-specific inference of differential expression in spatial transcriptomics.

A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types.

Spatial genomics enables multi-modal study of clonal heterogeneity in tissues.

The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations as well as the makeup of the tumour microenvironment. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts.

Compressed sensing for highly efficient imaging transcriptomics.

Recent methods for spatial imaging of tissue samples can identify up to ~100 individual proteins or RNAs at single-cell resolution. However, the number of proteins or genes that can be studied in these approaches is limited by long imaging times. Here we introduce Composite In Situ Imaging (CISI), a method that leverages structure in gene expression across both cells and tissues to limit the number of imaging cycles needed to obtain spatially resolved gene expression maps.

Robust decomposition of cell type mixtures in spatial transcriptomics.

A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets.

In situ genome sequencing resolves DNA sequence and structure in intact biological samples.

Understanding genome organization requires integration of DNA sequence and three-dimensional spatial context; however, existing genome-wide methods lack either base pair sequence resolution or direct spatial localization. Here, we describe in situ genome sequencing (IGS), a method for simultaneously sequencing and imaging genomes within intact biological samples. We applied IGS to human fibroblasts and early mouse embryos, spatially localizing thousands of genomic loci in individual nuclei.

Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2.

Measurement of the location of molecules in tissues is essential for understanding tissue formation and function. Previously, we developed Slide-seq, a technology that enables transcriptome-wide detection of RNAs with a spatial resolution of 10 μm. Here we report Slide-seqV2, which combines improvements in library generation, bead synthesis and array indexing to reach an RNA capture efficiency ~50% that of single-cell RNA-seq data (~10-fold greater than Slide-seq), approaching the detection efficiency of droplet-based single-cell RNA-seq techniques.

RNA timestamps identify the age of single molecules in RNA sequencing.

Current approaches to single-cell RNA sequencing (RNA-seq) provide only limited information about the dynamics of gene expression. Here we present RNA timestamps, a method for inferring the age of individual RNAs in RNA-seq data by exploiting RNA editing. To introduce timestamps, we tag RNA with a reporter motif consisting of multiple MS2 binding sites that recruit the adenosine deaminase ADAR2 fused to an MS2 capsid protein.

Efficient, continuous mutagenesis in human cells using a pseudo-random DNA editor.

Here we describe TRACE (T7 polymerase-driven continuous editing), a method that enables continuous, targeted mutagenesis in human cells using a cytidine deaminase fused to T7 RNA polymerase. TRACE induces high rates of mutagenesis over multiple cell generations in genes under the control of a T7 promoter integrated in the genome. We used TRACE in a MEK1 inhibitor-resistance screen, and identified functionally correlated mutations.

Expansion Microscopy: Scalable and Convenient Super-Resolution Microscopy.

Expansion microscopy (ExM) is a physical form of magnification that increases the effective resolving power of any microscope. Here, we describe the fundamental principles of ExM, as well as how recently developed ExM variants build upon and apply those principles. We examine applications of ExM in cell and developmental biology for the study of nanoscale structures as well as ExM's potential for scalable mapping of nanoscale structures across large sample volumes.

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.

Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq, we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus, characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, and defined the temporal evolution of cell type-specific responses in a mouse model of traumatic brain injury.