Isolate Cell-Type-Specific RNAs from Snap-Frozen Heterogeneous Tissue Samples without Cell Sorting

Author(s):  
Huifei Sophia Zheng ◽  
Chen-Che Jeff Huang
Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3025-3025
Author(s):  
Tran Bich Nguyen ◽  
Mamiko Sakata-Yanagimoto ◽  
Yukitsugu Asabe ◽  
Kenichi Yoshida ◽  
Hideharu Muto ◽  
...  

Abstract [Backgrounds] Angioimmunoblastic T-cell lymphoma (AITL) is a distinct subtype of peripheral T-cell lymphoma (PTCL), characterized by generalized lymphadenopathy and autoimmune-like manifestations. Regarding genetic lesions of AITL, frequent mutations in TET2, IDH2, DNMT3A and RHOA have been identified. In some PTCL cases, TET2 and DNMT3A mutations were identified in cell populations beyond the CD4+ T-lymphocytes, in which the tumor cells are contained, suggesting that TET2 and DNMT3A mutations occurred earlier than the commitment to CD4+ T lymphocytes. [Objective] We performed this study to identify the cell-type-specific mutations and further explore mutational profiles in AITL and AITL-related cancer. [Methods] The dataset of targeted sequencing was analyzed for 76 genes in 79 PTCL samples. Mutational origin was analyzed by cell sorting and laser microdissection. [Results] Targeted sequencing identified 168 mutations in 33 genes. Recurrent mutations, in addition to the already known frequent mutations in RHOA/TET2/IDH2/DNMT3A, were found in ODZ1 [4/79 (5%)], Notch1, NAV2, and MTERFD3 [3/79 (4%) for each], MLL2, TET3, FAT2, and LAMA2 [2/79 (3%) for each]. TET2/DMNT3A mutations showed statistically higher allelic burden than the newly identified mutations, suggesting precedence of TET2/DNMT3A mutations. Cell sorting and laser microdissection, followed by amplicon sequencing, revealed that TET2/DNMT3A mutations were identified in both tumor cell-enriched and –depleted populations while RHOA and IDH2 mutations were confined to tumor cell-enriched populations. Most of the newly identified mutations were similarly classified into the above-mentioned two types. It is noteworthy that we found some mutations only in T-cell lymphoma cell-depleted CD20-positive population but not in the tumor-cell-enriched PD-1-positive population. [Conclusion and discussion] Differentiation stages that mutational events arise are likely to be multiple in AITL and AITL-related lymphoma. Moreover, in AITL, Epstein-Bar virus-infected B cells often grow in an oligoclonal manner, sometimes resulting in monoclonal proliferation with fully malignant features. Detection of B-cell specific mutations might suggest premalignant status of B cells in these cases. Disclosures No relevant conflicts of interest to declare.


2017 ◽  
Author(s):  
Sebastian Preissl ◽  
Rongxin Fang ◽  
Yuan Zhao ◽  
Ramya Raviram ◽  
Yanxiao Zhang ◽  
...  

ABSTRACTGenome-wide analysis of chromatin accessibility in primary tissues has uncovered millions of candidate regulatory sequences in the human and mouse genomes1–4. However, the heterogeneity of biological samples used in previous studies has prevented a precise understanding of the dynamic chromatin landscape in specific cell types. Here, we show that analysis of the transposase-accessible-chromatin in single nuclei isolated from frozen tissue samples can resolve cellular heterogeneity and delineate transcriptional regulatory sequences in the constituent cell types. Our strategy is based on a combinatorial barcoding assisted single cell assay for transposase-accessible chromatin5 and is optimized for nuclei from flash-frozen primary tissue samples (snATAC-seq). We used this method to examine the mouse forebrain at seven development stages and in adults. From snATAC-seq profiles of more than 15,000 high quality nuclei, we identify 20 distinct cell populations corresponding to major neuronal and non-neuronal cell-types in foetal and adult forebrains. We further define cell-type specific cis regulatory sequences and infer potential master transcriptional regulators of each cell population. Our results demonstrate the feasibility of a general approach for identifying cell-type-specific cis regulatory sequences in heterogeneous tissue samples, and provide a rich resource for understanding forebrain development in mammals.


Development ◽  
2018 ◽  
Vol 145 (13) ◽  
pp. dev164640 ◽  
Author(s):  
Wayo Matsushima ◽  
Veronika A. Herzog ◽  
Tobias Neumann ◽  
Katharina Gapp ◽  
Johannes Zuber ◽  
...  

2016 ◽  
Author(s):  
Benedict Anchang ◽  
Sylvia K. Plevritis

AbstractCell sorting or gating homogenous subpopulations from single-cell data enables cell-type specific characterization, such as cell-type genomic profiling as well as the study of tumor progression. This highlight summarizes recently developed automated gating algorithms that are optimized for both population identification and sorting homogeneous single cells in heterogeneous single-cell data. Data-driven gating strategies identify and/or sort homogeneous subpopulations from a heterogeneous population without relying on expert knowledge thereby removing human bias and variability. We further describe an optimized cell sorting strategy called CCAST based on Clustering, Classification and Sorting Trees which identifies the relevant gating markers, gating hierarchy and partitions that define underlying cell subpopulations. CCAST identifies more homogeneous subpopulations in several applications compared to prior sorting strategies and reveals simultaneous intracellular signaling across different lineage subtypes under different experimental conditions.


2017 ◽  
Author(s):  
Wayo Matsushima ◽  
Veronika A Herzog ◽  
Tobias Neumann ◽  
Katharina Gapp ◽  
Johannes Zuber ◽  
...  

AbstractCell type-specific transcriptome analysis is an essential tool in understanding biological processes but can be challenging due to the limits of microdissection or fluorescence-activated cell sorting (FACS). Here, we report a novel in vivo sequencing method, which captures the transcriptome of a specific type of cells in a tissue without prior cellular or molecular sorting. SLAM-ITseq provides an accurate snapshot of the transcriptional state in vivo.


2012 ◽  
pp. 265-276 ◽  
Author(s):  
Aurelie Evrard ◽  
Bastiaan O. R. Bargmann ◽  
Kenneth D. Birnbaum ◽  
Mark Tester ◽  
Ute Baumann ◽  
...  

2018 ◽  
Author(s):  
Elior Rahmani ◽  
Regev Schweiger ◽  
Brooke Rhead ◽  
Lindsey A. Criswell ◽  
Lisa F. Barcellos ◽  
...  

AbstractHigh costs and technical limitations of cell sorting and single-cell techniques currently restrict the collection of large-scale, cell-type-specific DNA methylation data. This, in turn, impedes our ability to tackle key biological questions that pertain to variation within a population, such as identification of disease-associated genes at a cell-type-specific resolution. Here, we show mathematically and empirically that cell-type-specific methylation levels of an individual can be learned from its tissue-level bulk data, conceptually emulating the case where the individual has been profiled with a single-cell resolution and then signals were aggregated in each cell population separately. Provided with this unprecedented way to perform powerful large-scale epigenetic studies with cell-type-specific resolution, we revisit previous studies with tissue-level bulk methylation and reveal novel associations with leukocyte composition in blood and with rheumatoid arthritis. For the latter, we further show consistency with validation data collected from sorted leukocyte sub-types. Corresponding software is available from: https://github.com/cozygene/TCA.


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