scholarly journals SCENITH: A flow cytometry based method for functional profiling energy metabolism with single cell resolution

2020 ◽  
Author(s):  
Rafael J. Argüello ◽  
Alexis J. Combes ◽  
Remy Char ◽  
Evens Bousiquot ◽  
Julien-Paul Gigan ◽  
...  

AbstractEnergetic metabolism reprogramming is critical for cancer and immune responses. Current methods to functionally profile the global metabolic capacities and dependencies of cells are performed in bulk. We designed a simple method for complex metabolic profiling called SCENITH, for Single Cell ENergetIc metabolism by profilIng Translation inHibition. SCENITH allows for the study of metabolic responses in multiple cell types in parallel by flow cytometry. SCENITH is designed to perform metabolic studies ex vivo, particularly for rare cells in whole blood samples, avoiding metabolic biases introduced by culture media. We analyzed myeloid cells in solid tumors from patients and identified variable metabolic profiles, in ways that are not linked to their lineage nor their activation phenotype. SCENITH ability to reveal global metabolic functions and determine complex and linked immune-phenotypes in rare cell subpopulations will contribute to the information needed for evaluating therapeutic responses or patient stratification.

2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  

AbstractModern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e19013-e19013
Author(s):  
Marianne T. Santaguida ◽  
Ryosuke Kita ◽  
Steven A. Schaffert ◽  
Erica K. Anderson ◽  
Kamran A Ali ◽  
...  

e19013 Background: Understanding the heterogeneity of AML is necessary for developing targeted drugs and diagnostics. A key measure of heterogeneity is the variance in response to treatments. Previously, we developed an ex vivo flow cytometry drug sensitivity assay (DSA) that predicted response to treatments in myelodysplastic syndrome. Unlike bulk cell viability measures of other drug sensitivity assays, our flow cytometry assay provides single cell resolution. The assay measures a drug’s effect on the viability or functional state of specific cell types. Here we present the development of this technology for AML, with additional measurements of DNA-Seq and RNA-Seq. Using the data from this assay, we aim to characterize the heterogeneity in AML drug sensitivity and the molecular mechanisms that drive it. Methods: As an initial feasibility analysis, we assayed 1 bone marrow and 3 peripheral blood AML patient samples. For the DSA, the samples were cultured with six AML standard of care (SOC) compounds across seven doses, in addition to two combinations. The cells were stained to detect multiple cell types including tumor blasts, and drug response was measured by flow cytometry. For the multi-omics, the cells were magnetically sorted to enrich for blasts and then assayed using a targeted 400 gene DNA-Seq panel and whole bulk transcriptome RNA-Seq. For comparison with BeatAML, Pearson correlations between gene expression and venetoclax sensitivity were investigated. Results: In our drug sensitivity assay, we measured dose response curves for the six SOC compounds, for each different cell type across each sample. The dose responses had cell type specific effects, including differences in drug response between CD11b+ blasts, CD11b- blasts, and other non-blast populations. Integrating with the DNA-Seq and RNA-Seq data, known associations between ex vivo drug response and gene expression were identified with additional cell type specificity. For example, BCL2A1 expression was negatively correlated with venetoclax sensitivity in CD11b- blasts but not in CD11b+ blasts. To further corroborate, among the top 1000 genes associated with venetoclax sensitivity in BeatAML, 93.7% had concordant directionality in effect. Conclusions: Here we describe the development of an integrated ex vivo drug sensitivity assay and multi-omics dataset. The data demonstrated that ex vivo responses to compounds differ between cell types, highlighting the importance of measuring drug response in specific cell types. In addition, we demonstrated that integrating these data will provide unique insights on molecular mechanisms that affect cell type specific drug response. As we continue to expand the number of patient samples evaluated with our multi-dimensional platform, this dataset will provide insights for novel drug target discovery, biomarker development, and, in the future, informing treatment decisions.


2020 ◽  
Vol 52 (10) ◽  
pp. 468-477
Author(s):  
Alexander C. Zambon ◽  
Tom Hsu ◽  
Seunghee Erin Kim ◽  
Miranda Klinck ◽  
Jennifer Stowe ◽  
...  

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.


2021 ◽  
Author(s):  
Saptarshi Bej ◽  
Anne-Marie Galow ◽  
Robert David ◽  
Markus Wolfien ◽  
Olaf Wolkenhauer

AbstractThe research landscape of single-cell and single-nuclei RNA sequencing is evolving rapidly, and one area that is enabled by this technology, is the detection of rare cells. An automated, unbiased and accurate annotation of rare subpopulations is challenging. Once rare cells are identified in one dataset, it will usually be necessary to generate other datasets to enrich the analysis (e.g., with samples from other tissues). From a machine learning perspective, the challenge arises from the fact that rare cell subpopulations constitute an imbalanced classification problem.We here introduce a Machine Learning (ML)-based oversampling method that uses gene expression counts of already identified rare cells as an input to generate synthetic cells to then identify similar (rare) cells in other publicly available experiments. We utilize single-cell synthetic oversampling (sc-SynO), which is based on the Localized Random Affine Shadowsampling (LoRAS) algorithm. The algorithm corrects for the overall imbalance ratio of the minority and majority class.We demonstrate the effectiveness of the method for two independent use cases, each consisting of two published datasets. The first use case identifies cardiac glial cells in snRNA-Seq data (17 nuclei out of 8,635). This use case was designed to take a larger imbalance ratio (∼1 to 500) into account and only uses single-nuclei data. The second use case was designed to jointly use snRNA-Seq data and scRNA-Seq on a lower imbalance ratio (∼1 to 26) for the training step to likewise investigate the potential of the algorithm to consider both single cell capture procedures and the impact of “less” rare-cell types. For validation purposes, all datasets have also been analyzed in a traditional manner using common data analysis approaches, such as the Seurat3 workflow.Our algorithm identifies rare-cell populations with a high accuracy and low false positive detection rate. A striking benefit of our algorithm is that it can be readily implemented in other and existing workflows. The code basis is publicly available at FairdomHub (https://fairdomhub.org/assays/1368) and can easily be transferred to train other customized approaches.


Author(s):  
Jianxiong Tang ◽  
Jianxiao Zou ◽  
Mei Fan ◽  
Qi Tian ◽  
Jiyang Zhang ◽  
...  

Abstract Motivation Single-cell DNA methylation sequencing detects methylation levels with single-cell resolution, while this technology is upgrading our understanding of the regulation of gene expression through epigenetic modifications. Meanwhile, almost all current technologies suffer from the inherent problem of detecting low coverage of the number of CpGs. Therefore, addressing the inherent sparsity of raw data is essential for quantitative analysis of the whole genome. Results Here, we reported CaMelia, a CatBoost gradient boosting method for predicting the missing methylation states based on the locally paired similarity of intercellular methylation patterns. On real single-cell methylation datasets, CaMelia yielded significant imputation performance gains over previous methods. Furthermore, applying the imputed data to the downstream analysis of cell-type identification, we found that CaMelia helped to discover more intercellular differentially methylated loci that were masked by the sparsity in raw data, and the clustering results demonstrated that CaMelia could preserve cell-cell relationships and improve the identification of cell types and cell subpopulations. Availability and implementation Python code is available at https://github.com/JxTang-bioinformatics/CaMelia. Supplementary information Supplementary data are available at Bioinformatics online.


Author(s):  
Rasa Elmentaite ◽  
Alexander Ross ◽  
Kylie R. James ◽  
Daniel Ortmann ◽  
Tomas Gomes ◽  
...  

SummaryHuman gut development requires the orchestrated interaction of various differentiating cell types. Here we generate an in-depth single-cell map of the developing human intestine at 6–10 weeks post-conception, a period marked by crypt-villus formation. Our analysis reveals the transcriptional profile of cycling epithelial precursor cells, which are distinct from LGR5-expressing cells. We use computational analyses to show that these cells contribute to differentiated cell subsets directly and indirectly via the generation of LGR5-expressing stem cells and receive signals from the surrounding mesenchymal cells. Furthermore, we draw parallels between the transcriptomes of ex vivo tissues and in vitro fetal organoids, revealing the maturation of organoid cultures in a dish. Lastly, we compare scRNAseq profiles from paediatric Crohn’s disease epithelium alongside matched healthy controls to reveal disease associated changes in epithelial composition. Contrasting these with the fetal profiles reveals re-activation of fetal transcription factors in Crohn’s disease epithelium. Our study provides a unique resource, available at www.gutcellatlas.org, and underscores the importance of unravelling fetal development in understanding disease.


2019 ◽  
Author(s):  
Tanya T. Karagiannis ◽  
John P. Cleary ◽  
Busra Gok ◽  
Nicholas G. Martin ◽  
Elliot C. Nelson ◽  
...  

AbstractChronic opioid usage not only causes addiction behavior through the central nervous system (CNS), but it also modulates the peripheral immune system. However, whether opioid usage positively or negatively impacts the immune system is still controversial. In order to understand the immune modulatory effect of opioids in a systematic and unbiased way, we performed single cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) from opioid-dependent individuals and non-dependent controls. We show that chronic opioid usage evokes widespread suppression of interferon-stimulated genes (ISGs) and antiviral gene program in naive monocytes and upon ex vivo stimulation with the pathogen component lipopolysaccharide (LPS) in multiple innate and adaptive immune cell types. Furthermore, scRNA-seq revealed the same phenomenon with in vitro morphine treatment; after just a short exposure to morphine stimulation, we observed the same suppression of antiviral genes in multiple immune cell types. These findings indicate that both acute and chronic opioid exposure may be harmful to our immune system by suppressing the antiviral gene program, our body’s defense response to potential infection. Our results suggest that further characterization of the immune modulatory effects of opioid use is critical to ensure the safety of clinical opioid usage.


2021 ◽  
Author(s):  
Francisco J. Garcia ◽  
Na Sun ◽  
Hyeseung Lee ◽  
Brianna Godlewski ◽  
Kyriaki Galani ◽  
...  

SummaryDespite the importance of the blood-brain barrier in maintaining normal brain physiology and in understanding neurodegeneration and CNS drug delivery, human cerebrovascular cells remain poorly characterized due to their sparsity and dispersion. Here, we perform the first single-cell characterization of the human cerebrovasculature using both ex vivo fresh-tissue experimental enrichment and post mortem in silico sorting of human cortical tissue samples. We capture 31,812 cerebrovascular cells across 17 subtypes, including three distinct subtypes of perivascular fibroblasts as well as vasculature-coupled neurons and glia. We uncover human-specific expression patterns along the arteriovenous axis and determine previously uncharacterized cell type-specific markers. We use our newly discovered human-specific signatures to study changes in 3,945 cerebrovascular cells of Huntington’s disease patients, which reveal an activation of innate immune signaling in vascular and vasculature-coupled cell types and the concomitant reduction to proteins critical for maintenance of BBB integrity. Finally, our study provides a comprehensive resource molecular atlas of the human cerebrovasculature to guide future biological and therapeutic studies.


2019 ◽  
Author(s):  
Marc van Oostrum ◽  
Maik Müller ◽  
Fabian Klein ◽  
Roland Bruderer ◽  
Hui Zhang ◽  
...  

AbstractSystem-wide quantification of the cell surface proteotype and identification of extracellular glycosylation sites is challenging when sample is limiting. We miniaturized and automated the previously described Cell Surface Capture technology increasing sensitivity, reproducibility, and throughput. We used this technology, which we call autoCSC, to create population-specific surfaceome maps of developing mouse B cells and used targeted flow cytometry to uncover developmental cell subpopulations.


2020 ◽  
Author(s):  
Rafael Arguello ◽  
Alexis J. Combes ◽  
Remy Char ◽  
Evens Bousiquot ◽  
Julien P. Gigan ◽  
...  

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