David G. Priest

During antibody responses, germinal centers (GCs) act as a key site for somatic hypermutation (SHM) and affinity maturation of B cells. However, their lymphoid tissue localization has made experimental manipulation of human germinal center B (GCB) cells difficult. Here, we describe a simple in vitro method, modeling T cell–derived signaling, that converts human naive B cells into BCL6 ⁺ induced GCB-like (iGCB) cells. Through side-by-side proteomic and transcriptional single-cell analysis of iGCB and human tonsil GCB cells, we show that iGCB cells recapitulate the surface marker, transcription factor, DNA repair, and glycosylation signatures that define human GCB cells and display ongoing activation-induced cytidine deaminase (AID)–dependent SHM. iGCB cells can be generated from naive B cells and from unswitched memory/circulating marginal zone cells but not class-switched memory B cells. Using this system, we define signals critical for human GCB formation and identify interventions that block their induction, providing a simple and manipulable model of human GCB cell biology.

T follicular regulatory cells (Tfr) play a pivotal role in maintaining immune self-tolerance and prevention of autoantibody induction. However, the stages of Tfr formation have still not been clarified. Here, we found that 30 to 50% of circulating Tfr in human blood have a naïve-like phenotype (preTfr, CD45RA ⁺ CD45RO ⁻ Foxp3 ⁺ CXCR5 ⁺ ). PreTfr are expandable in vitro while retaining suppressive capacity. They are transcriptionally similar to naïve T _regs (nT _regs ) ex vivo, but after stimulation, they gain increased expression of Tfr-related suppressive molecules such as IL-1RA, suggesting that they are primed for full differentiation into mature Tfr. Further, we demonstrate that preTfr but not nT _reg are significantly reduced in the blood of patients with COVID-19 and patients with sepsis and that this loss is correlated with increased anti–interferon-γ antibodies and activated atypical B cells. Together, these data suggest that Tfr are disrupted at the earliest stage of their formation during severe disease and may be an important therapeutic target in future vaccine developments.

Accurate identification of immune cell subsets in single-cell RNA sequencing (scRNA-seq) data is critical for understanding immune responses in autoimmune diseases, infections, and cancer. One caveat of scRNA-seq is the inability to properly assign rare immune cell subsets due to gene dropout events. To circumvent this caveat, we here developed optimized detection and inference of names in scRNA-seq data (scODIN). scODIN uses an informed holistic 2-step approach combining expert knowledge with machine learning to rapidly assign cell type identities to large scRNA-seq datasets. First, scODIN uses key lineage-defining markers to identify a set of core cell types. Second, scODIN compensates for dropout events by integrating a k-nearest neighbors algorithm. We additionally programmed scODIN to detect dual and transitional phenotypes, which are usually overlooked in conventional analyses. Consequently, scODIN may enhance our understanding of immune cell heterogeneity and provide comprehensive insights into immune regulation, with broad implications for immunology and personalized medicine.

Regulatory T cells (Treg) play an important role in regulating immune homeostasis in health and disease. Traditionally their suppressive function has been assayed by mixing purified cell populations, which does not provide an accurate picture of a physiologically relevant response. To overcome this limitation, we here develop ‘single cell suppression profiling of human Tregs’ (scSPOT). scSPOT uses a 52-marker CyTOF panel, a cell division detection algorithm, and a whole PBMC system to assess the effect of Tregs on all other cell types simultaneously. In this head-to-head comparison, we find Tregs having the clearest suppressive effects on effector memory CD8 T cells through partial division arrest, cell cycle inhibition, and effector molecule downregulation. Additionally, scSPOT identifies a Treg phenotypic split previously observed in viral infection and propose modes of action by the FDA-approved drugs Ipilimumab and Tazemetostat. scSPOT is thus scalable, robust, widely applicable, and may be used to better understand Treg immunobiology and screen for therapeutic compounds.

Accurate identification of immune cell subsets in single-cell (sc)RNA-seq data is critical for understanding immune responses in autoimmune diseases, infections, and cancer. One caveat of scRNA-seq is the inability to properly assign rare immune cell subsets due to gene dropout events. To circumvent this caveat, we here developed Optimized Detection and Inference of Names in scRNA-seq data (scODIN). scODIN uses an informed holistic two-step approach combining expert knowledge with machine learning to rapidly assign cell type identities to large scRNA-seq dataset. First, scODIN uses key lineage-defining markers to identify a set of core cell types. Second, scODIN compensates for dropout events by integrating a k-nearest neighbors (kNN) algorithm. We additionally programmed scODIN to detect dual and transitional phenotypes, which are usually overlooked in conventional analyses. Consequently, scODIN may enhance our understanding of immune cell heterogeneity and provides comprehensive insights into immune regulation, with broad implications for immunology and personalized medicine.

Light sheet microscopy combines high sensitivity and imaging speeds with low photodamage, however a remaining hurdle to its widespread adoption is its requirement for special sample preparation. Our new microscope - planar illumination microscope for single molecule imaging for all purpose (PISA) - significantly relieves these restrictions to enable light sheet imaging of coverslip-mounted samples as commonly analyzed in biological microscopes. We show how this technology now permits high-throughput imaging using multi-well plates. Further, the ability of 3D single molecule imaging in microlitre or mm ³ scale volumes over the coverslip improves the sensitivity of biochemical gel-based fluorometric assays to atto/zepto molar levels. PISA will facilitate widespread adoption of light sheet imaging for a variety of biological, biochemical and medical investigations.

Resting memory B cells can be divided into classical or atypical groups, but the heterogenous marker expression on activated memory B cells makes similar classification difficult. Here, by longitudinal analysis of mass cytometry and CITE-seq data from cohorts with COVID-19, bacterial sepsis, or BNT162b2 mRNA vaccine, we observe that resting B cell memory consist of classical CD45RB⁺ memory and CD45RB^lo memory, of which the latter contains of two distinct groups of CD11c⁺ atypical and CD23⁺ non-classical memory cells. CD45RB levels remain stable in these cells after activation, thereby enabling the tracking of activated B cells and plasmablasts derived from either CD45RB⁺ or CD45RB^lo memory B cells. Moreover, in both COVID-19 patients and mRNA vaccination, CD45RB^lo B cells formed the majority of SARS-CoV2 specific memory B cells and correlated with serum antibodies, while CD45RB⁺ memory are activated by bacterial sepsis. Our results thus identify that stably expressed CD45RB levels can be exploited to trace resting memory B cells and their activated progeny, and suggest that atypical and non-classical CD45RB^lo memory B cells contribute to SARS-CoV-2 infection and vaccination.

Nucleosomes are the unitary structures of chromosome folding, and their arrangements are intimately coupled to the regulation of genome activities. Conventionally, structural analyses using electron microscopy and X-ray crystallography have been used to study such spatial nucleosome arrangements. In contrast, recent improvements in the resolution of sequencing-based methods allowed investigation of nucleosome arrangements separately at each genomic locus, enabling exploration of gene-dependent regulation mechanisms. Here, we review recent studies on nucleosome folding in chromosomes from these two methodological perspectives: conventional structural analyses and DNA sequencing, and discuss their implications for future research.

High-throughput microscopy of bacterial cells elucidated fundamental cellular processes including cellular heterogeneity and cell division homeostasis. Polydimethylsiloxane (PDMS)-based microfluidic devices provide advantages including precise positioning of cells and throughput, however device fabrication is time-consuming and requires specialised skills. Agarose pads are a popular alternative, however cells often clump together, which hinders single cell quantitation. Here, we imprint agarose pads with micro-patterned ‘capsules’, to trap individual cells and ‘lines’, to direct cellular growth outwards in a straight line. We implement this micro-patterning into multi-pad devices called CapsuleHotel and LineHotel for high-throughput imaging. CapsuleHotel provides ~65,000 capsule structures per mm² that isolate individual Escherichia coli cells. In contrast, LineHotel provides ~300 line structures per mm that direct growth of micro-colonies. With CapsuleHotel, a quantitative single cell dataset of ~10,000 cells across 24 samples can be acquired and analysed in under 1 hour. LineHotel allows tracking growth of > 10 micro-colonies across 24 samples simultaneously for up to 4 generations. These easy-to-use devices can be provided in kit format, and will accelerate discoveries in diverse fields ranging from microbiology to systems and synthetic biology.