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The ability to align individual cellular information from multiple experimental sources is fundamental for a systems-level understanding of biological processes. However, currently available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely on a large number of shared features across datasets for cell matching. This approach underperforms when applied to single-cell proteomic datasets due to the limited number of parameters simultaneously accessed and lack of shared markers across these experiments. Here, we introduce a cell-matching algorithm, matching with partial overlap (MARIO) that accounts for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multimodal methods, including spatial techniques and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via codetection by indexing imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid by cellular indexing of transcriptomes and epitopes by sequencing, revealing unique immune responses within the lung microenvironment of patients with COVID.

Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6 million spatially resolved single cells with dramatic time savings.

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell’s marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell–cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions.

Accurate and rapid identification of cell populations is key to discovering novelty in multidimensional single cell experiments. We present a population finding algorithm X-shift that can process large datasets using fast KNN estimation of cell event density and automatically arranges populations by a marker-based classification system. X-shift analysis of mouse bone marrow data resolved the majority of known and several previously undescribed cell populations. Interestingly, previously known cell populations, as well as intermediate cell populations in early hematopoietic development, were described via novel marker combinations that were defined via routes to their locations in expressed marker space. X-shift provides a rapid, reliable approach to managed cell subset analysis that maximizes automation that not only best mimics human intuition, but as we show provides access to novel insights that “prior knowledge” might prevent the researcher from visualizing.

To enable the detection of expression signatures specific to individual cells, we developed PLAYR (proximity ligation assay for RNA), a method for highly multiplexed transcript quantification by flow and mass cytometry that is compatible with standard antibody staining. When used with mass cytometry, PLAYR allowed for the simultaneous quantification of more than 40 different mRNAs and proteins. In primary cells, we quantified multiple transcripts, with the identity and functional state of each analyzed cell defined on the basis of the expression of a separate set of transcripts or proteins. By expanding high-throughput deep phenotyping of cells beyond protein epitopes to include RNA expression, PLAYR opens a new avenue for the characterization of cellular metabolism.

Methods to visualize, track, measure and perturb proteins in living cells are central to biomedicine's efforts to characterize and understand the spatial and temporal underpinnings of life inside cells. Although fluorescent proteins have revolutionized such studies, they have shortcomings, which have spurred the creation of alternative approaches to chemically label proteins in live cells. In this review we highlight research questions that can be addressed using site-specific chemical labeling and present a comparison of the various labeling techniques that have been developed. We also provide a 'roadmap' for selection of appropriate labeling techniques(s) and outline generalized strategies to validate and troubleshoot chemical labeling experiments.

Flow cytometry allows high-content, multiparameter analysis of single cells, making it a promising tool for drug discovery and profiling of intracellular signaling. To add high-throughput capacity to flow cytometry, we developed a cell-based multiplexing technique called fluorescent cell barcoding (FCB). In FCB, each sample is labeled with a different signature, or barcode, of fluorescence intensity and emission wavelengths, and mixed with other samples before antibody staining and analysis by flow cytometry. Using three FCB fluorophores, we were able to barcode and combine entire 96-well plates, reducing antibody consumption 100-fold and acquisition time to 5-15 min per plate. Using FCB and phospho-specific flow cytometry, we screened a small-molecule library for inhibitors of T cell-receptor and cytokine signaling, simultaneously determining compound efficacy and selectivity. We also analyzed IFN-gamma signaling in multiple cell types from primary mouse splenocytes, revealing differences in sensitivity and kinetics between B cells, CD4+ and CD4- T cells and CD11b-hi cells.

Zinc-finger DNA-binding domains are expressed on the surface of cells so that the cells can be tagged with fluorescently labeled oligonucleotides, enabling many possible applications in complex cell populations.

We generated a sequential reporter-enzyme luminescence (SRL) technology for in vivo detection of beta-galactosidase (beta-gal) activity. The substrate, a caged D-luciferin-galactoside conjugate, must first be cleaved by beta-gal before it can be catalyzed by firefly luciferase (FLuc) to generate light. As a result, luminescence is dependent on beta-gal activity. Using this technology, constitutive beta-gal activity in engineered cells and inducible tissue-specific beta-gal expression in transgenic mice can now be visualized noninvasively over time. A substantial advantage of beta-gal as a bioluminescent probe is that the enzyme retains full activity outside of cells, unlike FLuc, which requires intracellular cofactors. As a result, antibodies conjugated to the recombinant beta-gal enzyme can be used to detect endogenous cells and extracellular antigens in vivo. Thus, coupling the properties of FLuc to the advantages of beta-gal permits bioluminescent imaging applications that previously were not possible.