Lineage tracking reveals dynamic relationships of T cells in colorectal cancer
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ABSTRACT:
PROVIDER: PRJNA429424 | ENA |
REPOSITORIES: ENA
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Project description:T cells are central players in cancer immunotherapy1, yet some of their fundamental properties such as development and migration within tumours remain elusive. The enormous T cell receptor (TCR) repertoire, required for recognising foreign and self-antigens2,3, could serve as lineage tags to track these T cells in tumours4. Here, we obtained transcriptomes of 11,138 single T cells from 12 colorectal cancer (CRC) patients and developed STARTRAC (Single T-cell Analysis by Rna-seq and Tcr TRACking) indices to quantitatively analyse dynamic relationships among 20 identified T cell subsets with distinct functions and clonalities. While both CD8+ effector and ?exhausted? T cells exhibited high clonal expansion, they were independently connected with tumour-resident CD8+ effector memory cells, implicating a TCR-based fate decision. Of the CD4+ T cells, the majority of tumour-infiltrating Tregs showed clonal exclusivity, whereas certain Treg clones were developmentally linked to multiple TH clones. Notably, we identified two IFNG+ TH1-like clusters in tumours, the GZMK+ TEM and CXCL13+ TH1-like clusters, which were associated with distinct IFN-?-regulating transcription factors, EOMES/RUNX3 and BHLHE40, respectively. Only BHLHE40+ CXCL13+ TH1-like cells were preferentially enriched in tumours of microsatellite-instable (MSI) patients, which might explain their favourable response rates to immune-checkpoint blockade. Furthermore, we found IGFLR1 to be highly expressed in both BHLHE40+CXCL13+ TH1-like and CD8+ exhausted T cells and possessed co-stimulatory functions. Our integrated STARTRAC analyses provided a powerful avenue to comprehensively dissect the T cell properties in CRC, which could shed new insights into the dynamic relationships of T cells in other cancers.
2018-10-29 | GSE108989 | GEO
Project description:Dynamic transcriptomic tracking directs the treatment for acute leukemia of ambiguous lineage
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Project description:Acute leukemia of ambiguous lineage (ALAL) is a rare subtype of acute leukemia characterized by the lack of straightforward myeloid or lymphoid lineage commitment. Currently, no treatment guidelines are commonly-accepted for ALAL. Herein, we have established an molecular-guided therapeutic approach to classify ALAL as either possessing an transciptional profile with a tendency towards acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL). A three and a half years perspective study saw an ALAL patient failed to respond to the ALL-like treatment recommended in most retrospective studies. However, RNA-seq profiling of this ALAL, together with indepth comparative analysis with AML, ALL and healthy bone marrow samples, demonstrated that the ALAL was transcriptomically more similar to AML, and correspondingly a complete molecular remission (CMR) was achieved with AML-like treatment regimen. Moreover, tracking of transcriptomic profile changes at nine time points throughout the treatment successfully detected tumor relapse seven months earlier than conventional approaches. Under the guidance of this Dynamic Transcriptomic Tracking (DTT) approach, we present a further ALAL patient that reached CMR with an AML-like treatment regimen. DTT maybe beneficial in other leukemias that do not possess recommended treatment guidelines. We propose that DTT is condidered for perspective trials.
2023-12-23 | GSE121169 | GEO
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Project description:microRNA profile of TNM-specific sequence design and integrated network-based bioinformatic analysis in 20 Colorectal cancer patients(n=5 for each TNM stage) are carried out to find the dynamic miRNA expression pattern specific to TNM stage. Gene microrray experiment of the same samples were also performed. Two different platform of microarray data were analyzed together to uncover the potential biomarker for progression of colorectal cancer.
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Project description:Cerebral organoids display dynamic clonal growth with lineage replenishment
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Project description:Global gene profile of TNM-specific sequence design and integrated network-based bioinformatic analysis in 20 Colorectal cancer patients(n=5 for each TNM stage) are carried out to find the dynamic gene expression pattern specific to TNM stage. microRNA microrray experiment of the same samples were also performed. Two different platform of microarray data were analyzed together to uncover the potential biomarker for progression of colorectal cancer.
2015-09-30 | GSE41011 | GEO
Project description:Early human kidney development is poorly documented due to tissue inaccessibility and a lack of genetic tractability. Here we combine reprogramming, CRISPR/Cas9 gene-editing and organoid technologies to study the nephron lineage in a human context. We confirm the presence of a SIX2+ population in early kidney organoids with a transcriptional profile akin to human fetal nephron progenitors. Using lineage-tracing analyses, we show that SIX2-expressing cells contribute to nephron formation but not to the putative collecting duct epithelium. Labeling of SIX2+ cells at various time-points during organoid differentiation revealed a markedly reduced capacity for these cells to contribute to nephron formation over time. This suggests human kidney organoids lack a true nephron progenitor niche, as the developing kidney does in vivo, capable of both self-renewal and ongoing nephrogeneis. Nonetheless, human iPSC-derived kidney tissue maintains previously identified lineage relationships, which supports the utility of in vitro organoid models for interrogating the molecular and cellular basis of early human development.
2019-03-12 | GSE119561 | GEO
Project description:Direct lineage reprogramming involves the conversion of cellular identity. Single-cell technologies are useful for deconstructing the considerable heterogeneity that emerges during lineage conversion. However, lineage relationships are typically lost during cell processing, complicating trajectory reconstruction. Here we present ‘CellTagging’, a combinatorial cell-indexing methodology that enables parallel capture of clonal history and cell identity, in which sequential rounds of cell labelling enable the construction of multi-level lineage trees. CellTagging and longitudinal tracking of fibroblast to induced endoderm progenitor reprogramming reveals two distinct trajectories: one leading to successfully reprogrammed cells, and one leading to a ‘dead-end’ state, paths determined in the earliest stages of lineage conversion. We find that expression of a putative methyltransferase, Mettl7a1, is associated with the successful reprogramming trajectory; adding Mettl7a1 to the reprogramming cocktail increases the yield of induced endoderm progenitors. Together, these results demonstrate the utility of our lineage-tracing method for revealing the dynamics of direct reprogramming.
2018-09-25 | GSE99915 | GEO