Back

Clinical implementation of single-cell RNA sequencing using liver fine needle aspirate tissue sampling and centralized processing captures compartment specific immuno-diversity

Genshaft, A. S.; Subudhi, S.; Keo, A.; Sanchez Vasquez, J. D.; Conceicao-Neto, N.; Mahamed, D.; Boeijen, L. L.; Alatrakchi, N.; Oetheimer, C.; Vilme, M.; Drake, R.; Fleming, I.; Tran, N.; Tzouanas, C.; Joseph-Chazan, J.; Villanueva, M. A.; van de Werken, H. J. G.; van Oord, G. W.; Groothuismink, Z. M. A.; Beudeker, B. J.; Osmani, Z.; Nkongolo, S.; Mehrotra, A.; Feld, J.; Chung, R. T.; de Knegt, R. J.; Janssen, H. L. A.; Aerssens, J.; Bollekens, J.; Hacohen, N.; Lauer, G. M.; Boonstra, A.; Shalek, A. K.; Gehring, A.

2021-12-02 immunology
10.1101/2021.11.30.470634 bioRxiv
Show abstract

Blood samples are frequently collected in human studies of the immune system but poorly represent tissue-resident immunity. Understanding the immunopathogenesis of tissue-restricted diseases, such as chronic hepatitis B, necessitates direct investigation of local immune responses. We developed a workflow that enables frequent, minimally invasive collection of liver fine-needle aspirates in multi-site international studies and centralized single-cell RNA sequencing data generation using the Seq-Well S3 picowell-based technology. All immunological cell types were captured, including liver macrophages, and showed distinct compartmentalization and transcriptional profiles, providing a systematic assessment of the capabilities and limitations of peripheral blood samples when investigating tissue-restricted diseases. The ability to electively sample the liver of chronic viral hepatitis patients and generate high-resolution data will enable multi-site clinical studies to power fundamental and therapeutic discovery.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.