scholarly journals Spatial single-cell profiling of intracellular metabolomes in situ

2019 ◽  
Author(s):  
Luca Rappez ◽  
Mira Stadler ◽  
Sergio Triana ◽  
Prasad Phapale ◽  
Mathias Heikenwalder ◽  
...  
Keyword(s):  
Single Cell ◽  
Lipid Droplets ◽  
Spatial Information ◽  
Cellular Heterogeneity ◽  
Cell Contact ◽  
Cell Monolayers ◽  
Cellular Models ◽  
Single Cell Profiling ◽  
Cell Cell

SummaryThe recently unveiled extent of cellular heterogeneity demands for single-cell investigations of intracellular metabolomes to reveal their roles in intracellular processes, molecular microenvironment and cell-cell interactions. To address this, we developed SpaceM, a method for in situ spatial single-cell metabolomics of cell monolayers which detects >100 metabolites in >10000 individual cells together with fluorescence and morpho-spatial cellular features. We discovered that the intracellular metabolomes of co-cultured human HeLa cells and mouse NIH3T3 fibroblasts predict the cell type with 90.4% accuracy and revealed a short-distance metabolic intermixing between HeLa and NIH3T3. We characterized lipid classes composing lipid droplets in steatotic differentiated human hepatocytes, and discovered a preferential accumulation of long-chain phospholipids, a co-regulation of oleic and linoleic acids, and an association of phosphatidylinositol monophosphate with high cell-cell contact. SpaceM provides single-cell metabolic, phenotypic, and spatial information and enables spatio-molecular investigations of intracellular metabolomes in a variety of cellular models.

scholarly journals Single Cell Profiling of Acute Kidney Injury Reveals Novel Transcriptional Signatures, Mixed Identities and Epithelial-to-Stromal Crosstalk

2019 ◽  
Author(s):  
Valeria Rudman-Melnick ◽  
Mike Adam ◽  
Andrew Potter ◽  
Saagar M. Chokshi ◽  
Qing Ma ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Single Cell ◽  
Stromal Cells ◽  
Gene Activation ◽  
Kidney Injury ◽  
Rapid Decline ◽  
Renal Tubules ◽  
Developmental Gene ◽  
Single Cell Profiling

SummaryAcute kidney injury (AKI) is a rapid decline of renal function, with an incidence of up to 67% of intensive care unit patients. Current treatments are merely supportive, emphasizing the need for deeper understanding that could lead to improved therapies. We used single cell RNA sequencing, in situ hybridization and protein expression analyses to create comprehensive renal cell specific transcriptional profiles of multiple AKI stages. We revealed that AKI induces marked dedifferentiation, renal developmental gene activation and mixed identities in injured renal tubules. Moreover, we identified potential pathologic crosstalk between epithelial and stromal cells, and several novel genes involved in AKI. We also demonstrated the definitive effects of age on AKI outcome, and showed that renal developmental genes hold a potential as novel AKI markers. Moreover, our study provides the resource power which will aid in unraveling the molecular genetics of AKI.

scholarly journals Quantitative Single-Cell Transcript Assessment of Biomarkers Supports Cellular Heterogeneity in the Bovine IVD

2019 ◽  
Vol 6 (2) ◽  
pp. 42 ◽  
Author(s):  
Kangning Li ◽  
Devin Kapper ◽  
Sumona Mondal ◽  
Thomas Lufkin ◽  
Petra Kraus
Keyword(s):  
In Situ Hybridization ◽  
Single Cell ◽  
Gaussian Mixture ◽  
Cellular Heterogeneity ◽  
Rna In Situ Hybridization ◽  
Ivd Degeneration

Severe and chronic low back pain is often associated with intervertebral disc (IVD) degeneration. While imposing a considerable socio-economic burden worldwide, IVD degeneration is also severely impacting on the quality of life of affected individuals. Cell-based regenerative medicine approaches have moved into clinical trials, yet IVD cell identities in the mature disc remain to be fully elucidated and tissue heterogeneity exists, requiring a better characterization of IVD cells. The bovine coccygeal IVD is an accepted research model to study IVD mechano-biology and disc homeostasis. Recently, we identified novel IVD biomarkers in the outer annulus fibrosus (AF) and nucleus pulposus (NP) of the mature bovine coccygeal IVD through RNA in situ hybridization (AP-RISH) and z-proportion test. Here we follow up on Lam1, Thy1, Gli1, Gli3, Noto, Ptprc, Scx, Sox2 and Zscan10 with fluorescent RNA in situ hybridization (FL-RISH) and confocal microscopy. This permits sub-cellular transcript localization and the addition of quantitative single-cell derived values of mRNA expression levels to our previous analysis. Lastly, we used a Gaussian mixture modeling approach for the exploratory analysis of IVD cells. This work complements our earlier cell population proportion-based study, confirms the previously proposed biomarkers and indicates even further heterogeneity of cells in the outer AF and NP of a mature IVD.

scholarly journals Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

Cells ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1751 ◽  
Author(s):  
Rishikesh Kumar Gupta ◽  
Jacek Kuznicki
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Future Directions ◽  
Strong Basis ◽  
Bodily Fluids ◽  
Single Cell Rna Sequencing

The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer’s disease). We outline scRNA-seq techniques that are suitable for investigating cellular heterogeneity that is present in cell populations with very high resolution of the transcriptomic landscape. We summarize scRNA-seq findings and applications of this technology to identify cell types, activity, and other features that are important for the function of different bodily organs. We discuss future directions for scRNA-seq techniques that can link gene expression, protein expression, cellular function, and their roles in pathology. We speculate on how the field could develop beyond its present limitations (e.g., performing scRNA-seq in situ and in vivo). Finally, we discuss the integration of machine learning and artificial intelligence with cutting-edge scRNA-seq technology, which could provide a strong basis for designing precision medicine and targeted therapy in the future.

Single cell 3-D platform to study ligand mobility in cell–cell contact

Lab on a Chip ◽  
2011 ◽  
Vol 11 (17) ◽  
pp. 2876 ◽  
Author(s):  
Mirjam Andreasson-Ochsner ◽  
Gregory Romano ◽  
Maria Håkanson ◽  
Michael L. Smith ◽  
Deborah E. Leckband ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Contact ◽  
Cell Cell

scholarly journals Spatial analysis of ligand-receptor interactions in skin cancer at genome-wide and single-cell resolution

2020 ◽  
Author(s):  
M Tran ◽  
S Yoon ◽  
ST Min ◽  
S Andersen ◽  
K Devitt ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
Digital Pcr ◽  
Droplet Digital Pcr ◽  
Cell Interactions ◽  
Genome Wide ◽  
Receptor Interactions ◽  
Cell Cell

AbstractThe ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is important to understand molecular mechanisms of cancer immunotherapy and drug targets. Current experimental methods such as immunohistochemistry allow researchers to investigate a small number of cells or a limited number of ligand-receptor pairs at tissue scale with limited cellular resolution. In this work, we developed a powerful experimental and analytical pipeline that allows for the genome-wide discovery and targeted validation of cellular communication. By profiling thousands of genes, spatial transcriptomic and single-cell RNA sequencing data show genes that are possibly involved in interactions. The expression of the candidate genes could be visualized by single-molecule in situ hybridization and droplet digital PCR. We developed a computational pipeline called STRISH that enables us to quantitatively model cell-cell interactions by automatically scanning for local expression of RNAscope data to recapitulate an interaction landscape across the whole tissue. Furthermore, we showed the strong correlation of microscopic RNAscope imaging data analyzed by STRISH with the gene expression values measured by droplet digital PCR. We validated the unique ability of this approach to discover new cell-cell interactions in situ through analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma. We expect that the approach described here will help to discover and validate ligand receptor interactions in different biological contexts such as immune-cancer cell interactions within a tumor.

scholarly journals Spatial charting of single cell transcriptomes in tissues

2021 ◽  
Author(s):  
Nicholas Navin ◽  
Runmin Wei ◽  
Siyuan He ◽  
Shanshan Bai ◽  
Emi Sei ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Spatial Information ◽  
Ductal Carcinoma ◽  
Single Cells ◽  
Cell Types ◽  
Spatial Structures ◽  
Tissue Sections ◽  
Normal Mouse Brain

Single cell RNA sequencing (scRNA-seq) methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics (ST) assays can profile spatial regions in tissue sections, but do not have single cell genomic resolution. Here, we developed a computational approach called SChart, that combines these two datasets to achieve single cell spatial mapping of cell types, cell states and continuous phenotypes. We applied SChart to reconstruct cellular spatial structures in existing datasets from normal mouse brain and kidney tissues to validate our approach. We also performed scRNA-seq and ST experiments on two ductal carcinoma in situ (DCIS) tissues and applied SChart to identify subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data shows that SChart can accurately map single cells in diverse tissue types to resolve their spatial organization into cellular neighborhoods and tissue structures.

scholarly journals Scalable in situ single-cell profiling by electrophoretic capture of mRNA

2022 ◽  
Author(s):  
Lars Borm ◽  
Alejandro Mossi Albiach ◽  
Camiel CA Mannens ◽  
Jokubas Janusauskas ◽  
Ceren Özgün ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Tissue Samples ◽  
Rna Molecules ◽  
Processing Data ◽  
Single Cell Profiling ◽  
Instrument Control ◽  
Hardware Specification ◽  
Control Image

Methods to spatially profile the transcriptome are dominated by a trade-off between resolution and throughput. Here, we developed a method named EEL FISH that can rapidly process large tissue samples without compromising spatial resolution. By electrophoretically transferring RNA from a tissue section onto a capture surface, EEL speeds up data acquisition by reducing the amount of imaging needed, while ensuring that RNA molecules move straight down towards the surface, preserving single-cell resolution. We applied EEL on eight entire sagittal sections of the mouse brain and measured the expression patterns of up to 440 genes to reveal complex tissue organisation. Moreover, EEL enabled the study of challenging human samples by removing autofluorescent lipofuscin, so that we could study the spatial transcriptome of the human visual cortex. We provide full hardware specification, all protocols and complete software for instrument control, image processing, data analysis and visualization.

scholarly journals Automated cell-type classification in intact tissues by single-cell molecular profiling

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Monica Nagendran ◽  
Daniel P Riordan ◽  
Pehr B Harbury ◽  
Tushar J Desai
Keyword(s):  
Single Cell ◽  
Spatial Information ◽  
Housekeeping Genes ◽  
Cell Types ◽  
Molecular Profiling ◽  
Mouse Lung ◽  
Intact Mouse ◽  
Specificity And Sensitivity

A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) with exceptional signal strength, specificity, and sensitivity in tissue. Multiplexed data sets can be acquired using barcoded probes and rapid label-image-erase cycles, with automated calculation of single cell profiles, enabling clustering and anatomical re-mapping of cells. We apply PLISH to expression profile ~2900 cells in intact mouse lung, which identifies and localizes known cell types, including rare ones. Unsupervised classification of the cells indicates differential expression of ‘housekeeping’ genes between cell types, and re-mapping of two sub-classes of Club cells highlights their segregated spatial domains in terminal airways. By enabling single cell profiling of various RNA species in situ, PLISH can impact many areas of basic and medical research.

Progesterone and cell–cell adhesion interact to regulate rat granulosa cell apoptosis

1994 ◽  
Vol 72 (11-12) ◽  
pp. 547-551 ◽  
Author(s):  
John J. Peluso ◽  
Anna Pappalardo
Keyword(s):  
Cell Adhesion ◽  
Granulosa Cell ◽  
Granulosa Cells ◽  
Cell Apoptosis ◽  
Cell Contact ◽  
Ru 486 ◽  
Sucrose Solutions ◽  
Rat Ovary ◽  
Cell Cell

Ovarian follicles are composed of both small and large granulosa cells, but only the large granulosa cells undergo apoptosis within 24 h of culture in serum-free medium. The present study was designed to assess the relationship between cell–cell contact, progesterone treatment, and granulosa cell apoptosis. For this study, individual large granulosa cells were isolated from immature rat ovaries after sequential incubation with EGTA and EGTA–sucrose solutions. Granulosa cells were then cultured for 24 h in RPMI-1640 (control) supplemented with progesterone and (or) the progesterone antagonist RU 486. The cells were then fixed and assessed for apoptosis by either electron microscopy or in situ end labeling of DNA fragments. After 24 h of culture, the proportion of apoptotic granulosa cells was twofold lower for aggregated cells compared with single granulosa cells (p < 0.05). Aggregated granulosa cells were observed to be connected by gap junctions. Compared with controls, progesterone reduced and RU 486 increased the percentage of single and aggregated apoptotic granulosa cells present after culture. In the presence of RU 486, progesterone reduced the percentage of apoptotic single granulosa cells from 84 ± 4% (RU 486 alone) to 66 ± 8%. In granulosa cell aggregates, progesterone reduced the incidence of apoptosis from 86 ± 3% to 44 ± 7% (p < 0.05). Progesterone in the presence of RU 486 was more effective in inhibiting apoptosis of aggregated granulosa cells than in single granulosa cells (p < 0.05). Taken together, these data indicate that (i) progesterone acts through the progesterone receptor to inhibit granulosa cell apoptosis and (ii) cell–cell adhesion enhances progesterone's anti-apoptotic actions.Key words: rat, ovary, granulosa cell, apoptosis.

scholarly journals Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 28
Author(s):  
Shruti Gupta ◽  
Ajay Kumar Verma ◽  
Shandar Ahmad
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Single Cell ◽  
Gene Selection ◽  
Spatial Information ◽  
Single Cells ◽  
Drosophila Embryo ◽  
Main Challenge ◽  
Selection For

Single-cell transcriptomics data, when combined with in situ hybridization patterns of specific genes, can help in recovering the spatial information lost during cell isolation. Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium conducted a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC) to predict the masked locations of single cells from a set of 60, 40 and 20 genes out of 84 in situ gene patterns known in Drosophila embryo. We applied a genetic algorithm (GA) to predict the most important genes that carry positional and proximity information of the single-cell origins, in combination with the base distance mapping algorithm DistMap. Resulting gene selection was found to perform well and was ranked among top 10 in two of the three sub-challenges. However, the details of the method did not make it to the main challenge publication, due to an intricate aggregation ranking. In this work, we discuss the detailed implementation of GA and its post-challenge parameterization, with a view to identify potential areas where GA-based approaches of gene-set selection for topological association prediction may be improved, to be more effective. We believe this work provides additional insights into the feature-selection strategies and their relevance to single-cell similarity prediction and will form a strong addendum to the recently published work from the consortium.

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