Tonsils are model secondary lymphoid organs (SLO), as they are the first line of defense against many pathogens and are easily accessible. Thus, a complete map of these cells is key to understand how adaptive immunity develops, and to map the cell-of-origin of many lymphomas
2/n
In this setting, we were standing on the shoulders of GIANTS. @hamish_king@louisakjames@WJGreenleaf and colleagues published in 2021 two seminal papers describing the gene expression and open chromatin dynamics during B cell activation at unprecedented resolution.
3/n
We aimed to complement and expand on their findings following a multimodal approach, and focusing also on other cell types. Our atlas comprises 10 donors, 121 cell types and states, >357,000 cells, and 5 data modalities, allowing to map all "basis vectors" of cell identity.
4/n
Importantly, the dataset of @hamish_king et al. served as an external control to ensure we were correcting for technical effects while preserving biological heterogeneity. Indeed, we were able to find 13 and 72 precursor T and B cells (out of 357K cells!)
5/n
We annotated 14 cell states of CD4 T cells. These states recapitulate T follicular helper (Tfh) cell differentiation in SLO, as described by @profshanecrotty in the last decade. Strikingly, we discover a Tfh-specific superenhancer that potentially controls BCL6 expression.
6/n
Similarly, we find 19 types of cytotoxic cells (CD8 T, NK, ILC), including CXCL13-expressing CD8 T follicular cells (CD8 Tf). CD8 Tf express PD1 and ICOS at both RNA and protein levels, are clonally expanded and display a high activity of motifs of the NFAT family. 7/n
In the B cell compartment, we characterize the expression and chromatin dynamics along the light zone (LZ) to dark zone (DZ) reentry. NFKB is followed by BATF in this trajectory.
8/n
We discover SIX5 as a previosly uncharacterized transcription factor (TF) that regulates plasma cell (PC) maturation. We validate these findings at both transcriptomic and epigenomic levels in 4 independent datasets, including multiple myeloma (a PC-derived tumor). 9/n
FDCSP was first described in follicular dendritic cells (FDC) and in “leukocyte-infiltrated tonsillar crypts” , although the population within the crypts remained unknown. We show that FDSCP expressing cells represent a specific cell type of the tonsillar epithelium 10/n
In 2020, Bianchetto-Aguilera et al. described tonsillar slan+ cells as a cell type derived from non-classical slan+ monocytes that display a gene expression signature different from tonsillar macrophages and DC. Are tonsillar slan+ a homogeneous or heterogeneous population? 11/n
We found 4 markedly different subsets of tonsillar slan+ cells, which we coin as SLANCYTES. Slancyte subsets differed in their antigen-presenting, ECM dissassembly, cytokine production, spatial localization and cell-cell interactome, among others.
12/n
We aimed to make our dataset as FAIR (findable, accessible, interoperable, resusable) as possible. We developed HCATonsilData, a package that provides access to the tonsil atlas data in a modular and programmatic fashion. Few lines of code and one obtains a SCE object.
13/n
We also develop SLOcatoR, an R package used to annotate single-cell transcriptomes and open chromatin profiles from SLO using the tonsil atlas as reference. SLOcatoR identified an enrichment of Tregs in the microenvironment of mantle cell lymphoma.
14/n
We provide a glossary with the reasoning, markers, and evidence we followed to annotate each and everyone of the 121 cell types in our atlas. As annotations are dynamic by nature, we will provide new versions via the HCATonsilData package.
15/n
Transparency and reproducibility are at the heart of both the @humancellatlas and the @hoheyn labs. We provide access to the data in 5 different levels (from raw to processed). We also release 5 GitHub repos with all the scripts, notebooks, reports, and shiny apps used here
16/n
This has been a Herculean effort that involves the work of many bright people. Special thanks to co-first authors Paula Soler, @saguilarfer and @JuanCNieto1 for sharing this experience with me till the very end.
We really hope you will like and enjoy it!
17/n
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First, you need to master the experimental protocol. My top pick here is this tutorial by @Ati_lz & @Moutinho_C. I highly suggest reading the glossary to get familiar with the jargon.
I would also suggest this classic benchmark by @chris_zie and @B_Vieth, which provides a clear comparison of the different steps in the different scRNA-seq protocols.