Cell Surface Protein mRNAs Show Differential Transcription in Pyramidal and Fast-Spiking Cells as Revealed by Single-Cell Sequencing

The prefrontal cortex (PFC) plays a key role in higher order cognitive functions and psychiatric disorders such as autism, schizophrenia, and depression. In the PFC, the two major classes of neurons are the glutamatergic pyramidal (Pyr) cells and the GABAergic interneurons such as fast-spiking (FS) cells. Despite extensive electrophysiological, morphological, and pharmacological studies of the PFC, the therapeutically utilized drug targets are restricted to dopaminergic, glutamatergic, and GABAergic receptors. To expand the pharmacological possibilities as well as to better understand the cellular and network effects of clinically used drugs, it is important to identify cell-type-selective, druggable cell surface proteins and to link developed drug candidates to Pyr or FS cell targets. To identify the mRNAs of such cell-specific/enriched proteins, we performed ultra-deep single-cell mRNA sequencing (19 685 transcripts in total) on electrophysiologically characterized intact PFC neurons harvested from acute brain slices of mice. Several selectively expressed transcripts were identified with some of the genes that have already been associated with cellular mechanisms of psychiatric diseases, which we can now assign to Pyr (e.g., Kcnn2, Gria3) or FS (e.g., Kcnk2, Kcnmb1) cells. The earlier classification of PFC neurons was also confirmed at mRNA level, and additional markers have been provided

Proteomics of spatially identified tissues in whole organs

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of biological specimens imaged in 3D as a whole are lacking. Here, we present DISCO-MS, a technology combining whole-organ imaging, deep learning-based image analysis, and ultra-high sensitivity mass spectrometry. DISCO-MS yielded qualitative and quantitative proteomics data indistinguishable from uncleared samples in both rodent and human tissues. Using DISCO-MS, we investigated microglia activation locally along axonal tracts after brain injury and revealed known and novel biomarkers. Furthermore, we identified initial individual amyloid-beta plaques in the brains of a young familial Alzheimer’s disease mouse model, characterized the core proteome of these aggregates, and highlighted their compositional heterogeneity. Thus, DISCO-MS enables quantitative, unbiased proteome analysis of target tissues following unbiased imaging of entire organs, providing new diagnostic and therapeutic opportunities for complex diseases, including neurodegeneration

A guidebook for DISCO tissue clearing

Abstract Histological analysis of biological tissues by mechanical sectioning is significantly time‐consuming and error‐prone due to loss of important information during sample slicing. In the recent years, the development of tissue clearing methods overcame several of these limitations and allowed exploring intact biological specimens by rendering tissues transparent and subsequently imaging them by laser scanning fluorescence microscopy. In this review, we provide a guide for scientists who would like to perform a clearing protocol from scratch without any prior knowledge, with an emphasis on DISCO clearing protocols, which have been widely used not only due to their robustness, but also owing to their relatively straightforward application. We discuss diverse tissue‐clearing options and propose solutions for several possible pitfalls. Moreover, after surveying more than 30 researchers that employ tissue clearing techniques in their laboratories, we compiled the most frequently encountered issues and propose solutions. Overall, this review offers an informative and detailed guide through the growing literature of tissue clearing and can help with finding the easiest way for hands‐on implementation

Deep learning-enabled multi-organ segmentation in whole-body mouse scans

Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prerequisite for quantitative analysis but is a tedious and error-prone task if done manually. Here, we present a deep learning solution called AIMOS that automatically segments major organs (brain, lungs, heart, liver, kidneys, spleen, bladder, stomach, intestine) and the skeleton in less than a second, orders of magnitude faster than prior algorithms. AIMOS matches or exceeds the segmentation quality of state-of-the-art approaches and of human experts. We exemplify direct applicability for biomedical research for localizing cancer metastases. Furthermore, we show that expert annotations are subject to human error and bias. As a consequence, we show that at least two independently created annotations are needed to assess model performance. Importantly, AIMOS addresses the issue of human bias by identifying the regions where humans are most likely to disagree, and thereby localizes and quantifies this uncertainty for improved downstream analysis. In summary, AIMOS is a powerful open-source tool to increase scalability, reduce bias, and foster reproducibility in many areas of biomedical research

Spatial proteomics in three-dimensional intact specimens

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT