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Immunofluorescence staining allows detection and localization of antigens in different tissue types providing high sensitivity. The indirect immunofluorescence protocol is based on the principle of using a primary antibody binding to the target epitope, and a fluorophore-tagged secondary antibody that recognizes and binds to the primary antibody. This methods provides signal amplification allowing detection of several targets in the same tissue sample. This detailed protocol describes an adapted protocol, from An atlas of the protein-coding genes in the human, pig, and mouse brain article, for tissue staining in Tissue MicroArrays (TMA) and whole tissue Formalin Fixed Paraffin Embedded (FFPE) sections which is used in Emma Lundberg research group at Science for Life Laboratory; KTH - Royal Institute of Technology.

The lung contains numerous specialized cell types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a comprehensive topographic atlas of early human lung development. Here we report 83 cell states and several spatially resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated single-cell RNA sequencing and spatially resolved transcriptomics into a web-based, open platform for interactive exploration. We show distinct gene expression programmes, accompanying sequential events of cell differentiation and maturation of the secretory and neuroendocrine cell types in proximal epithelium. We define the origin of airway fibroblasts associated with airway smooth muscle in bronchovascular bundles and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.

CODEX technology allows for highly multiplexed analysis of + 40 proteins on the same section relying on DNA-conjugated antibodies being commercialized by Akoya Biosciences (former name CODEX®, current name PhenoCycler™). This detailed protocol describes an adapted protocol, from CODEX User Manual Rev C , for tissue staining in Tissue MicroArrays (TMA) and whole tissue Formalin Fixed Paraffin Embedded (FFPE) sections to run into the CODEX system which is used in Emma Lundberg research group at Science for Life Laboratory; KTH - Royal Institute of Technology.

Microscopy-based spatially resolved omic methods are transforming biology and medicine. Currently, these methods rely on high magnification objectives and cannot resolve crowded molecular targets, which limits the amount of biological information that can be extracted from a sample. To overcome these limitations, we developed Deconwolf (DW), an open-source software enabling high-performance deconvolution of widefield fluorescence microscopy image stacks and large tissue scans on a laptop computer. DW significantly outperformed two popular deconvolution tools on images generated by standard immunofluorescence as well as on images of crowded diffraction limited fluorescence dots generated by single-molecule fluorescence in situ hybridization (smFISH) and high-definition DNA FISH. In addition, widefield imaging followed by DW produced images comparable, if not superior in quality to confocal microscopy, but more than 200 times faster. Application of DW to smFISH images enabled accurate quantification of Ki-67 gene transcripts across a tumor microarray tissue core imaged with a 20x air objective. Finally, we applied DW to deconvolve images generated by in situ spatial transcriptomics (ISST) and in situ genomics by OligoFISSEQ. In ISST, DW increased the number of transcripts identified more than three times, while its application to OligoFISSEQ images drastically improved the efficiency of chromosome tracing without the need for signal interpolation. We conclude that DW greatly facilitates the use of deconvolution in many bioimaging applications and paves the way to the application of microscopy-based spatially resolved omic technologies in diagnostics.

SummaryThe lung contains numerous specialized cell-types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a first comprehensive topographic atlas of early human lung development. We report 83 cell states, several spatially-resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated scRNA-Seq and spatial transcriptomics into a web-based, open platform for interactive exploration. To illustrate the utility of our approach we show distinct states of secretory and neuroendocrine cells, largely overlapping with the programs activated either during lung fibrosis or small cell lung cancer progression. We define the origin of uncharacterized airway fibroblasts associated with airway smooth muscle in bronchovascular bundles, and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.

The Human Protein Atlas provides a map showing the distribution and relative abundance of proteins in the human body. All IHC staining in the Human Protein Atlas project are performed using the following standard protocol. The primary antibody dilution is based on titration optimization, the dilution suggested by the Human Protein Atlas can be found under antibody and antigen information for each antibody. When primary antibody originates from other host animals than rabbit, there are some modifications and different secondary antibody is used.

Sample preparation for single nuclei sequencing using fresh frozen tissue from human postmortem brain. The protocol is a combination of an already existing and published protocol in Nature protocols Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons and the 10x Genomics handbook Sample preparation, isolation of nuclei for single nuclei RNA sequencing.

International audience; Deep learning methods have shown extraordinary potential for analyzing very diverse biomedical data, but their dissemination beyond developers is hindered by important computational hurdles. We introduce ImJoy (https://imjoy.io/), a flexible and open-source browser-based platform designed to facilitate widespread reuse of deep learning solutions in biomedical research. We highlight ImJoy's main features and illustrate its functionalities with deep learning plugins for mobile and interactive image analysis and genomics. Deep learning methods, which use artificial neural networks to learn complex mappings between numerical data, have enabled recent breakthroughs in a wide range of biomedical data analysis tasks. Examples for imaging data include image segmentation 1,2 and medical diagnosis, where deep learning vastly outperforms more traditional methods and often exceeds human expert performance 3,4 , or methods to enhance microscopy images, e.g. for denoising or 1