Multiplexed mRNA profiling in the spatial context provides new information enabling basic research and clinical applications. Unfortunately, existing spatial transcriptomics methods are limited due to either low multiplexing or complexity. Here, we introduce a spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based decoding. We demonstrate MOSAICA’s multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of five fluorophores with facile error-detection and removal of autofluorescence. MOSAICA’s analysis is strongly correlated with sequencing data (Pearson’s r = 0.96) and was further benchmarked using RNAscopeTM and LGC StellarisTM. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues. We finally demonstrate simultaneous co-detection of protein and mRNA in cancer cells.

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To image 4-plex immunofluorescence-stained tissue samples at a low cost with cellular level resolution and sensitivity and dynamic range required to detect lowly and highly abundant targets, here we describe a robust, inexpensive (<$9000), 3D printable portable imaging device (Tissue Imager). The Tissue Imager can immediately be deployed on benchtops for in situ protein detection in tissue samples. Applications for this device are broad, ranging from answering basic biological questions to clinical pathology, where immunofluorescence can detect a larger number of markers than the standard H&E or chromogenic immunohistochemistry (CIH) staining, while the low cost also allows usage in classrooms. After characterizing our platform’s specificity and sensitivity, we demonstrate imaging of a 4-plex immunology panel in human cutaneous T-cell lymphoma (CTCL) formalin-fixed paraffin-embedded (FFPE) tissue samples. From those images, positive cells were detected using CellProfiler, a popular open-source software package, for tumor marker profiling. We achieved a performance on par with commercial epifluorescence microscopes that are >10 times more expensive than our Tissue Imager. This device enables rapid immunofluorescence detection in tissue sections at a low cost for scientists and clinicians and can provide students with a hands-on experience to understand engineering and instrumentation. We note that for using the Tissue Imager as a medical device in clinical settings, a comprehensive review and approval processes would be required.

Multiplexed mRNA profiling in the spatial context provides important new information enabling basic research and clinical applications. Unfortunately, most existing spatial transcriptomics methods are limited due to either low multiplexing or assay complexity. Here, we introduce a new spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based target decoding. This technology is the first application combining the biophotonic techniques; Spectral and Fluorescence Lifetime Imaging and Microscopy (FLIM), to the field of spatial transcriptomics. By integrating the time dimension with conventional spectrum-based measurements, MOSAICA enables direct, highly-multiplexed, in situ spatial biomarker profiling in a single round of staining and imaging while providing error correction removal of background autofluorescence. We demonstrate mRNA encoding using combinatorial spectral and lifetime labeling and target decoding and quantification using a phasor-based image segmentation and machine learning clustering technique. We then showcase MOSAICA′s multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of only five fluorophores with facile error-correction and removal of autofluorescent moieties. MOSAICA′s analysis is strongly correlated with sequencing data (Pearson′s r = 0.9) and was further benchmarked using RNAscope™ and LGC Stellaris™. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues that have a high degree of tissue scattering and intrinsic autofluorescence, demonstrating the robustness of the approach. MOSAICA represents a simple, versatile, and scalable tool for targeted spatial transcriptomics analysis that can find broad utility in constructing human cell atlases, elucidating biological and disease processes in the spatial context, and serving as companion diagnostics for stratified patient care.

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