Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100A^hi/HLA-DR^lo classical monocytes and activated LAG-3^hi T cells are hallmarks of progressive disease and highlights the abnormal MHC-II/LAG-3 interaction on myeloid and T cells, respectively. We also find skewed T cell receptor repertories in expanded effector CD8⁺ clones, unmutated IGHG⁺ B cell clones, and mutated B cell clones with stable somatic hypermutation frequency over time. In conclusion, our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19.

Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their B cell receptor (BCR) sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse junction region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the junction sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture junction similarity and shared mutations, and combine these in a spectral clustering framework to infer the BCR clonal relationships. Using both simulated and experimental data, we show that this model improves both the sensitivity and specificity for identifying B cell clones. Source code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or newer) in the Immcantation framework: www.immcantation.org under the AGPLv3 license. B cells recognize antigens through their BCR. During adaptive immune responses, antigen-specific B cells undergo intense proliferation. This B cell clonal expansion is coupled with a process of SHM, which results in the accumulation of mutations in the DNA encoding the BCR. Within the specialized micro-environment of the germinal center, these diversified B cells compete for antigen binding and presentation to follicular helper T cells. Successful binding leads to repeated cycles of proliferation, SHM and affinity-dependent selection ultimately resulting in the generation of high-affinity memory and antibody-secreting plasma cells. Driven by dramatic improvements in high-throughput sequencing technologies, large-scale characterization of BCR repertoires is now feasible. However, a critical barrier to quantitative analysis of these large-scale BCR repertoire data is the accurate identification of B cell clones. B cells are inferred to be clonally related if the distance between their BCR sequences is close enough. This paper develops a hybrid distance function that integrates information from the V(D)J recombination process (distance between CDR3 sequences), along with information from a common history of clonal expansion (shared SHMs in the V and J segments of the BCR) to improve the ability to identify clonally related sequences.

Following antigenic challenge, activated B cells rapidly expand and undergo somatic hypermutation, yielding groups of clonally related B cells with diversified immunoglobulin receptors. Inference of clonal relationships based on the receptor sequence is an essential step in many adaptive immune receptor repertoire sequencing studies. These relationships are typically identified by a multi-step process that involves: (i) grouping sequences based on shared V and J gene assignments, and junction lengths and (ii) clustering these sequences using a junction-based distance. However, this approach is sensitive to the initial gene assignments, which are error-prone, and fails to identify clonal relatives whose junction length has changed through accumulation of indels. Through defining a translation-invariant feature space in which we cluster the sequences, we develop an alignment free clonal identification method that does not require gene assignments and is not restricted to a fixed junction length. This alignment free approach has higher sensitivity compared to a typical junction-based distance method without loss of specificity and PPV. While the alignment free procedure identifies clones that are broadly consistent with the junction-based distance method, it also identifies clones with characteristics (multiple V or J gene assignments or junction lengths) that are not detectable with the junction-based distance method.