scholarly journals Flow-FISH as a Tool for Studying Bacteria, Fungi and Viruses

BioTech ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 21
Author(s):  
Julian J. Freen-van Heeren

Many techniques are currently in use to study microbes. These can be aimed at detecting, identifying, and characterizing bacterial, fungal, and viral species. One technique that is suitable for high-throughput analysis is flow cytometry-based fluorescence in situ hybridization, or Flow-FISH. This technique employs (fluorescently labeled) probes directed against DNA or (m)RNA, for instance targeting a gene or microorganism of interest and provides information on a single-cell level. Furthermore, by combining Flow-FISH with antibody-based protein detection, proteins of interest can be measured simultaneously with genetic material. Additionally, depending on the type of Flow-FISH assay, Flow-FISH can also be multiplexed, allowing for the simultaneous measurement of multiple gene targets and/or microorganisms. Together, this allows for, e.g., single-cell gene expression analysis or identification of (sub)strains in mixed cultures. Flow-FISH has been used in mammalian cells but has also been extensively employed to study diverse microbial species. Here, the use of Flow-FISH for studying microorganisms is reviewed. Specifically, the detection of (intracellular) pathogens, studying microorganism biology and disease pathogenesis, and identification of bacterial, fungal, and viral strains in mixed cultures is discussed, with a particular focus on the viruses EBV, HIV-1, and SARS-CoV-2.

2021 ◽  
Author(s):  
Qiang Li ◽  
Zuwan Lin ◽  
Ren Liu ◽  
Xin Tang ◽  
Jiahao Huang ◽  
...  

AbstractPairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introducein situelectro-sequencing (electro-seq), combining soft bioelectronics within situRNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We appliedin situelectro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodalin situanalysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis,in situelectro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation,in situelectro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4107-4107
Author(s):  
Tanaya Shree ◽  
Anuja Sathe ◽  
Debra K. Czerwinski ◽  
Steven R. Long ◽  
Hanlee Ji ◽  
...  

Abstract The critical determinants of effective antitumor immune responses, whether native or induced by therapy, remain poorly understood due to the complexity and plasticity of the immune system. To better profile and track these responses, we have employed the novel approach of performing single cell RNA sequencing for paired gene expression and immune repertoire analysis on tumor fine needle aspirates and peripheral blood of lymphoma patients undergoing immunotherapy on a clinical trial (NCT02927964). In this in situ vaccination study, patients with low-grade lymphoma received local low-dose radiation and intratumoral SD-101 (a TLR9 agonist) to one site of disease, with systemic ibrutinib (a BTK inhibitor) added in the second week of treatment. Tumor fine needle aspirates and peripheral blood samples were obtained prior to treatment, at one week (prior to ibrutinib initiation) and at six weeks after treatment start. Single cell preparations were processed using 10X Genomics' single cell RNA transcription and library preparation protocol, followed by sequencing on the Illumina platform. Cells were sequenced to an average depth of 50,000 reads/cell for gene expression libraries and 5000 reads/cell for TCR sequencing. Identification of variable genes, principal component and/or canonical correlation analysis, graph-based clustering and differential expression analysis of single-cell gene expression data was performed using the Seurat algorithm. Single-cell TCR repertoires were analyzed using TCR-specific analysis software. This data is being integrated with data from multiparameter flow cytometry and functional immune assays for these same patients, as well as with their clinical outcomes. Sequencing libraries have been prepared from 37 samples from 4 patients thus far. We have successfully generated single cell gene expression and TCR libraries from 3,000-10,000 cells from tumor fine needle aspirates and peripheral blood, with excellent sequencing quality metrics obtained. From detailed analyses of one patient's samples thus far, we have identified distinct immune populations in blood and tumor (Figure 1), including light-chain restricted B-cells, with good concordance with flow cytometry. Preliminary results show changes occurring in immune cell frequencies and phenotypes at the treated tumor site, at distant tumor sites and in the peripheral blood when samples from before and after treatment are compared. Sample collection, sequencing, and analysis are ongoing. Deep profiling of serial biopsies during immunotherapy using single cell RNA sequencing promises to illuminate underlying cellular dynamics, and paired with clinical outcome data, determinants of response. Ultimately, this may provide a roadmap for successful translation of single-cell genomics into the clinic for treatment monitoring and response prediction. Disclosures No relevant conflicts of interest to declare.


2010 ◽  
Vol 397 (5) ◽  
pp. 1853-1859 ◽  
Author(s):  
Yongzhong Li ◽  
Hansa Thompson ◽  
Courtney Hemphill ◽  
Fan Hong ◽  
Jessica Forrester ◽  
...  

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