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Single-cell transcriptomics has the potential to provide novel insights into poorly studied microbial eukaryotes. Although several such technologies are available and benchmarked on mammalian cells, few have been tested on protists. Here, we optimized a microarray single-cell sequencing (MASC-seq) technology that generates microscope images of cells in parallel with capturing their transcriptomes. We tested the method on three species representing important plankton groups with different cell structures, the ciliateTetrahymena thermophila, the diatomPhaeodactylum tricornutumand the dinoflagellateHeterocapsasp.. Both the cell fixation and permeabilization steps were adjusted. For the ciliate and dinoflagellate, the number of transcripts of microarray spots with single cells were significantly higher than for background spots, and the overall expression patterns were correlated with that of bulk RNA, while for the much smaller diatom cells, it was not possible to separate single-cell transcripts from background. The MASC-seq method holds promise for investigating “microbial dark matter”, although further optimizations are necessary to increase the signal-to-noise ratio.

Several important human infectious diseases are caused by microscale-sized parasitic nematodes like filarial worms. Filarial worms have their own spatial tissue organization and to uncover this tissue structure, we need methods that can spatially resolve these miniature specimens. Most filarial worms evolved a mutualistic association with endosymbiotic bacteriaWolbachia, however, the mechanisms underlying the dependency of filarial worms on the fitness of these bacteria remain unknown. AsWolbachiais essential for the development, reproduction, and survival of filarial worms, we focused on studying a posterior region containing reproductive tissue and developing embryos of adult femaleBrugia malayiworms. To spatially explore howWolbachiainteracts with the worm’s reproductive system, we performed a spatial characterization using Spatial Transcriptomics (ST) across our region of interest. We provide a proof-of-concept for miniature-ST to explore spatial gene expression patterns in small sample types, demonstrating the method’s ability to uncover nuanced tissue region expression patterns, observe the spatial localization of keyB. malayi-Wolbachiapathway genes, and co-localize theB. malayispatial transcriptome inWolbachiatissue regions. We envision our approach to open up new scenarios for the study of infectious diseases caused by micro-scale parasitic worms.

The spatial distribution of lymphocyte clones within tissues is critical to their development, selection, and expansion. We have developed Spatial Transcriptomics of VDJ sequences (Spatial VDJ), which maps immunoglobulin and TR antigen receptors in human tissue sections. Spatial VDJ captures lymphocyte clones matching canonical T, B, and plasma cell distributions in tissues and amplifies clonal sequences confirmed by orthogonal methods. We confirm spatial congruency between paired receptor chains, develop a computational framework to predict receptor pairs, and link the expansion of distinct B cell clones to different tumor-associated gene expression programs. Spatial VDJ delineates B cell clonal diversity, class switch recombination, and lineage trajectories within their spatial context. Taken together, Spatial VDJ captures lymphocyte spatial clonal architecture across tissues, which could have important therapeutic implications.One-Sentence SummarySpatial transcriptomics-based technology co-captures T and B cell receptors within their anatomical niche in human tissue.

All multicellular organisms are closely associated with microbes, which have a major impact on the health of their host. The interactions of microbes among themselves and with the host take place at the microscale, forming complex networks and spatial patterns that are rarely well understood due to the lack of suitable analytical methods. The importance of high-resolution spatial molecular information has become widely appreciated with the recent advent of spatially resolved transcriptomics. Here, we present Spatial metaTranscriptomics (SmT), a sequencing-based approach that leverages 16S/18S/ITS/poly-d(T) multimodal arrays for simultaneous host transcriptome- and microbiome-wide characterization of tissues at 55-µm resolution. We showcase SmT in outdoor-grown Arabidopsis thaliana leaves as a model system, and found tissue-scale bacterial and fungal hotspots. By network analysis, we study inter- and intra-kingdom spatial interactions among microbes, as well as the host response to microbial hotspots. SmT is a powerful new strategy that will be pivotal to answering fundamental questions on host-microbiome interplay.

Reconstruction of heterogeneity through single-cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we implement Spatial Transcriptomics to perform transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development and regeneration as well as its potential in pre-clinical and clinical pathology.

Reconstruction of spatial heterogeneity through single-cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we implement Spatial Transcriptomics to perform global transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development, and regeneration as well as being an asset in pre-clinical and clinical pathology. This repository contains original data such as count matrices, spot coordinate files, Hematoxylin &Eosin stained images, vein-masks, etc.Sample 1 to sample 3 refers to the individual ST experiments performed in this study. Sample 1 consists of 3 sections of the murine caudate lobe. Sample 2 consists of 4 sections of the right lobe, of which to were excluded for analysis. Sample 3 consists of 5 sections of the caudate lobe, of which two were excluded for analysis. These folders contain input data for the R markdown script, which can be found in this GitHub repository:https://github.com/almaan/ST-mLiver. The Hepaquery folder contains all data necessary to reproduce the results of the study and instructions on how to install the python package and reproduce the data of this study can also be found in the same GitHub repository (https://github.com/almaan/ST-mLiver). The folder termed "Immunofluorescence" contains all original images from the orthogonal validations of vein types (Supplementary Figure 22.1-22.2) in "Veins" and cell count estimations "Celltypes" by immunostaining. The Celltypes folder contains a subfolder "manual counting", with the randomly selected spots after overlaying 100 µm spots on the images (Supplementary figure 21). &nbsp

Reconstruction of heterogeneity through single cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we apply Spatial Transcriptomics to perform transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development and regeneration as well as its potential in pre-clinical and clinical pathology., Global transcriptional differences across lobular units in the liver remain unknown. Here the authors perform spatial transcriptomics of liver tissue to delineate transcriptional differences in physical space, confirm lobular zonation along transcriptional gradients and suggest the presence of previously uncharacterized structures within liver tissue.

Background Interest in studying the spatial distribution of gene expression in tissues is rapidly increasing. Spatial Transcriptomics is a novel sequencing-based technology that generates high-throughput information on the distribution, heterogeneity and co-expression of cells in tissues. Unfortunately, manual preparation of high-quality sequencing libraries is time-consuming and subject to technical variability due to human error during manual pipetting, which results in sample swapping and the accidental introduction of batch effects. All these factors complicate the production and interpretation of biological datasets. Results We have integrated an Agilent Bravo Automated Liquid Handling Platform into the Spatial Transcriptomics workflow. Compared to the previously reported Magnatrix 8000+ automated protocol, this approach increases the number of samples processed per run, reduces sample preparation time by 35%, and minimizes batch effects between samples. The new approach is also shown to be highly accurate and almost completely free from technical variability between prepared samples. Conclusions The new automated Spatial Transcriptomics protocol using the Agilent Bravo Automated Liquid Handling Platform rapidly generates high-quality Spatial Transcriptomics libraries. Given the wide use of the Agilent Bravo Automated Liquid Handling Platform in research laboratories and facilities, this will allow many researchers to quickly create robust Spatial Transcriptomics libraries.

The common glow-worms (Lampyris noctiluca) are best known for emission of green light by their larvae and sexually active adult females. However, both their DNA and RNA viruses remain unknown. Glow-worms are virologically interesting, as they are non-social and do not feed as adults, and hence their viral transmission may be limited. We identified viral sequences from 11 different virus taxa by the RNA-sequencing of two Finnish populations of adult glow-worms. The viruses represent nine different virus families and have negative, positive, or double-stranded RNA genomes. We also found a complete retroviral genome. Similar viral sequences were found from the sequencing data of common eastern firefly of North America, a species belonging to the same family (Lampyridae) as that of the common glow-worm. On average, an individual glow-worm had seven different RNA virus types and most of them appeared to establish a stable infection since they were found from glow-worms during two consecutive years. Here we present the characterization of load, prevalence, and interactions for each virus. Most of the glow-worm RNA viruses seem to be transmitted vertically, which may reflect the biology of glow-worms as non-social capital breeders, i.e., they invest stored resources in reproduction. Electronic supplementary material The online version of this article (10.1007/s11262-019-01724-5) contains supplementary material, which is available to authorized users.

Paralysis occurring in amyotrophic lateral sclerosis (ALS) results from denervation of skeletal muscle as a consequence of motor neuron degeneration. Interactions between motor neurons and glia contribute to motor neuron loss, but the spatiotemporal ordering of molecular events that drive these processes in intact spinal tissue remains poorly understood. Here, we use a spatially resolved view of disease-driven gene expression changes to stratify these events, reveal the relevant sub-populations of cells involved in each stage of disease progression, and characterize the underlying molecular mechanisms that trigger and drive the course of disease. Based on the well characterized cellular organization of the spinal cord and the importance of intercellular interactions in ALS disease progression, we applied spatial transcriptomics (ST) to obtain spatially and anatomically resolved quantitative gene expression measurements of mouse spinal cords over the course of disease, as well as in postmortem tissue from ALS patients. We developed a novel Bayesian generative model for assembling a spatiotemporal atlas of gene expression in ALS that integrates cell-type, anatomical region, space, and time. We identify novel pathways implicated in ALS progression, key differences between microglia and astrocyte populations at early time-points and in different anatomical regions, and discern several transcriptional pathways shared between murine models of ALS and human postmortem spinal cords. We provide a general experimental-computational design for mapping and understanding the transcriptional landscape of diseases in complex tissues. An interactive data exploration portal for our ST analysis is available at als-st.nygenome.org.