CD7 protein plays a crucial role in T cell infiltration in tumors

Sheng, Binjie; Zhang, Kailu; Tian, Shuaiyu; Ma, Renyuxue; Li, Zixuan; Wu, Hai Hong; Jiang, Licui; You, Fengming; An, Gangli; Meng, Hua; Yang, Lin; Liu, Xin

doi:10.1016/j.heliyon.2023.e16961

Cited by 1 publication

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, of the remaining 43%, more than half were included in the probe set, although not detected in the probe-based samples according to the detection threshold used. These consisted mainly of mitochondria-related genes, though a few immune-related genes were detected such as Csf1 , characteristic of red pulp macrophages (32), and Cd7 expressed in T cells (33). OCT-only genes that were not found in the probe set include Hba-a1 expressed in erythrocytes (34), and Ccl19 and Ccl21a involved in T cell immune responses (35).…”

Section: Resultsmentioning

confidence: 99%

Spotlight on 10x Visium: a multi-sample protocol comparison of spatial technologies

Du,

Wang,

Law

et al. 2024

Preprint

View full text Add to dashboard Cite

BackgroundSpatial transcriptomics allows gene expression to be measured within complex tissue contexts. Among the array of spatial capture technologies available is 10x Genomics’ Visium platform, a popular method which enables transcriptomewide profiling of tissue sections. Visium offers a range of sample handling and library construction methods which introduces a need for benchmarking to compare data quality and assess how well the technology can recover expected tissue features and biological signatures.ResultsHere we presentSpatialBench, a unique reference dataset generated from spleen tissue of mice responding to malaria infection spanning several tissue preparation protocols (both fresh frozen and FFPE samples, with and without CytAssist tissue placement). We noted better quality control metrics in reference samples prepared using probe-based capture methods, particularly those processed with CytAssist, validating the improvement in data quality produced with the platform. Our analysis of replicate samples extends to explore spatially variable gene detection, the outcomes of clustering and cell deconvolution using matched single-cell RNA-sequencing data and publicly available reference data to identify cell types and tissue regions expected in the spleen. Multi-sample differential expression analysis recovered known gene signatures related to biological sex or gene knockout.ConclusionsWe framed a comprehensive multi-sample analysis workflow that allowed us to generate consistent results both within and between different subsets of replicate samples, enabling broader comparisons and interpretations to be made at the group-level. OurSpatialBenchdataset, analysis, and workflow can serve as a practical guide for Visium users and may prove valuable in other benchmarking studies.

show abstract