A single-cell atlas of lymphocyte adaptive immune repertoires and transcriptomes reveals age-related differences in convalescent COVID-19 patients

Abstract

COVID-19 disease outcome is highly dependent on adaptive immunity from T and B lymphocytes, which play a critical role in the control, clearance and long-term protection against SARS-CoV-2. To date, there is limited knowledge on the composition of the T and B cell immune receptor repertoires [T cell receptors (TCRs) and B cell receptors (BCRs)] and transcriptomes in convalescent COVID-19 patients of different age groups. Here, we utilize single-cell sequencing (scSeq) of lymphocyte immune repertoires and transcriptomes to quantitatively profile the adaptive immune response in COVID-19 patients of varying age. We discovered highly expanded T and B cells in multiple patients, with the most expanded clonotypes coming from the effector CD8+ T cell population. Highly expanded CD8+ and CD4+ T cell clones show elevated markers of cytotoxicity (CD8: PRF1, GZMH, GNLY; CD4: GZMA), whereas clonally expanded B cells show markers of transition into the plasma cell state and activation across patients. By comparing young and old convalescent COVID-19 patients (mean ages = 31 and 66.8 years, respectively), we found that clonally expanded B cells in young patients were predominantly of the IgA isotype and their BCRs had incurred higher levels of somatic hypermutation than elderly patients. In conclusion, our scSeq analysis defines the adaptive immune repertoire and transcriptome in convalescent COVID-19 patients and shows important age-related differences implicated in immunity against SARS-CoV-2.

Passive Immunity Should and Will Work for COVID-19 for Some Patients

“…Cohen et al [31] portray a scenario where differential T cell responses give rise to varying disease susceptibility for young and older ages. The latter is also supported by other age-related differences shown for B and T cell clonal expansions after infection [114]. These variations on age-related responses may also be superimposed on the differential character of innate immune reactivity [115].…”

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

“…This information can be used to explore how various parameters dictating inference of phylogenetic trees and similarity networks relate to either the immune repertoire or gene expression phenotypes (DeWitt et al, 2018). Although we have provided default parameter values derived from experimental sequencing data from 10x Genomics and recently published data (Kuhn et al, 2021;Bieberich et al, 2021;Neumeier, Pedrioli, et al, 2021). Echidna similarly allows the user to customize each feature of the simulation, including the initial clonal diversity generated via V(D)J recombination, clonal expansion, transcriptional states, and their respective transition probabilities, and, for B cells, both SHM and isotype distributions.…”

“…During the time-resolved simulation of the immune receptors, each cell is additionally associated with a corresponding transcriptome containing gene expression information. The transcriptomes are simulated using either user-supplied distributions or transcriptional distributions based on publicly available single-cell immune repertoire sequencing data from 10x Genomics or recent publications (Bieberich et al, 2021;Kuhn et al, 2021;Neumeier, Pedrioli, et al, 2021). It is additionally possible to supply a finite number of distinct transcriptional phenotypes and a corresponding vector, thereby dictating the average expression for each gene in each phenotype.…”

“…(iv) Phenotypes switch randomly following either a default transition matrix or user-supplied transition matrix. To generate default gene expression vectors, we incorporated data from either 10x Genomics or previous single-cell immune repertoire sequencing studies containing either human or mouse B and T cells (Table S3) (Bieberich et al, 2021;Kuhn et al, 2021;Neumeier, Pedrioli, et al, 2021). The data was prepared by first pooling all cells (separately for human and mice) and subsequently sorting cells of desired phenotypic markers.…”

A need of COVID19 vaccination for children aged <12 years: Comparative evidence from the clinical characteristics in patients during a recent Delta surge (B.1.617.2)

“…A total of 226 infections were enrolled, of which 77 cases were under the age of 12 years (34.07%), with 44 males (57.14%) and a median age of 9 [6,9], and 149 cases were aged ≥12 years (65.93%), with 50 males (33.56%) and a median age of 39 [32,50]. The children aged <12y were unvaccinated and had no underlying conditions, while 94.63% (141/149) of patients aged ≥12 years were vaccinated, of whom 9 (6.04%) received one dose of vaccine and 132 (88.59%) received two doses of vaccine and 18 (12.08%) reported coexisting conditions (11 diabetes, 6 hypertension, and 1 chronic kidney disease) (Table 1…”