Flow Cytometry Data Preparation Guidelines for Improved Automated Phenotypic Analysis

Imbalance of SARS-CoV-2-specific CCR6+ and CXCR3+ CD4+ T cells and IFN-γ + CD8+ T cells in patients with Long-COVID

Long-COVID (LC) is characterised by persistent symptoms for at least 3 months after acute infection. A dysregulation of the immune system and a persistent hyperinflammatory state may cause LC. LC patients present differences in activation and exhaustion states of innate and adaptive compartments. Different T CD4+ cell subsets can be identified by differential expression of chemokine receptors (CCR). However, changes in T cells with expression of CCRs such as CCR6 and CXCR3 and their relationship with CD8+ T cells remains unexplored in LC. Here, we performed unsupervised analysis and found CCR6+ CD4+ subpopulations enriched in COVID-19 convalescent individuals upon activation with SARS-CoV-2 peptides. SARS-CoV-2 specific CCR6+ CD4+ are decreased in LC patients, whereas CXCR3+ CCR6- and CCR4+ CCR6- CD4+ T cells are increased. LC patients showed lower IFN-γ-secreting CD8+ T cells after stimulation with SARS-CoV-2 Spike protein. This work underscores the role of CCR6 in the pathophysiology of LC.

T Cell Homeostasis Disturbances in a Cohort of Long-Term Elite Controllers of HIV Infection

Elite controllers (ECs) are people living with HIV (PLWH) able to control HIV replication without antiretroviral therapy and have been proposed as a model of a functional HIV cure. Much evidence suggests that this spontaneous control of HIV has a cost in terms of T cell homeostasis alterations. We performed a deep phenotypic study to obtain insight into T cell homeostasis disturbances in ECs maintaining long-term virologic and immunologic control of HIV (long-term elite controllers; LTECs). Forty-seven PLWH were included: 22 LTECs, 15 non-controllers under successful antiretroviral therapy (onART), and 10 non-controllers not receiving ART (offART). Twenty uninfected participants (UCs) were included as a reference. T cell homeostasis was analyzed by spectral flow cytometry and data were analyzed using dimensionality reduction and clustering using R software v3.3.2. Dimensionality reduction and clustering yielded 57 and 54 different CD4 and CD8 T cell clusters, respectively. The offART group showed the highest perturbation of T cell homeostasis, with 18 CD4 clusters and 15 CD8 clusters significantly different from those of UCs. Most of these alterations were reverted in the onART group. Interestingly, LTECs presented several disturbances of T cell homeostasis with 15 CD4 clusters and 13 CD8 clusters different from UC. Moreover, there was a specific profile of T cell homeostasis alterations associated with LTECs, characterized by increases in clusters of naïve T cells, increases in clusters of non-senescent effector CD8 cells, and increases in clusters of central memory CD4 cells. These results demonstrate that, compared to ART-mediated control of HIV, the spontaneous control of HIV is associated with several disturbances in CD4 and CD8 T cell homeostasis. These alterations could be related to the existence of a potent and efficient virus-specific T cell response, and to the ability to halt disease progression by maintaining an adequate pool of CD4 T cells.

Transcriptional Behavior of Regulatory T Cells Predicts IBD Patient Responses to Vedolizumab Therapy

BACKGROUND

Inflammatory bowel disease (IBD) involves chronic T cell-mediated inflammatory responses. Vedolizumab (VDZ), a monoclonal antibody against α4β7 integrin, inhibits lymphocyte extravasation into intestinal mucosae and is effective in ulcerative colitis (UC) and Crohn's disease (CD).

AIM

We sought to identify immune cell phenotypic and gene expression signatures that related to response to VDZ.

METHODS

Peripheral blood (PBMC) and lamina propria mononuclear cells (LPMCs) were analyzed by flow cytometry and Cytofkit. Sorted CD4 + memory (Tmem) or regulatory T (Treg) cells from PBMC and LPMC were analyzed by RNA sequencing (RNA-seq). Clinical response (≥2-point drop in partial Mayo scores [UC] or Harvey-Bradshaw index [CD]) was assessed 14 to 22 weeks after VDZ initiation. Machine-learning models were used to infer combinatorial traits that predicted response to VDZ.

RESULTS

Seventy-one patients were enrolled: 37 received VDZ and 21 patients remained on VDZ >2 years. Fourteen of 37 patients (38%; 8 UC, 6 CD) responded to VDZ. Immune cell phenotypes and CD4 + Tmem and Treg transcriptional behaviors were most divergent between the ileum and colon, irrespective of IBD subtype or inflammation status. Vedolizumab treatment had the greatest impact on Treg metabolic pathways, and response was associated with increased expression of genes involved in oxidative phosphorylation. The strongest clinical predictor of VDZ efficacy was concurrent use of thiopurines. Mucosal tissues offered the greatest number of response-predictive biomarkers, whereas PBMC Treg-expressed genes were the best predictors in combinatorial models of response.

CONCLUSIONS

Mucosal and peripheral blood immune cell phenotypes and transcriptional profiles can inform VDZ efficacy and inform new opportunities for combination therapies.

The evolution of spectral flow cytometry

This special issue of Cytometry marks the transition of spectral flow cytometry from an emerging technology into a transformative force that will shape the fields of cytometry and single-cell analysis for some time to come. Tracing its roots to the earliest years of flow cytometry, spectral flow cytometry has evolved from the domain of individual researchers pushing the limits of hardware, reagents, and software to the mainstream, where it is being harnessed and adapted to meet the analytical challenges presented by modern biomedical research. In particular, the current form of spectral flow technology has arisen to address the needs of multiparameter immunophenotyping of immune cells in basic and translational research, and much of the current instrumentation and software reflects the needs of those applications. Yet, the possibilities enabled by high-resolution analysis of the spectral properties of optical absorbance, scatter, and emission have only begun to be exploited. In this brief review, the author highlights the origins and early milestones of single-cell spectral analysis, assesses the current state of instrumentation and software, and speculates as to future directions of spectral flow cytometry technology and applications.

How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed, as opposed to the common practice of manual gating in conventional flow cytometry. However, preparing spectral flow cytometry data for high-dimensional analysis can be challenging, because of several technical aspects. In this article, we will give insight into the pitfalls of handling spectral flow cytometry datasets. Moreover, we will describe a workflow to properly prepare spectral flow cytometry data for high dimensional analysis and tools for integrating new data at later time points. Using healthy control data as example, we will go through the concepts of quality control, data cleaning, transformation, correcting for batch effects, subsampling, clustering and data integration. This methods article provides an R-based pipeline based on previously published packages, that are readily available to use. Application of our workflow will aid spectral flow cytometry users to obtain valid and reproducible results.

Characteristic pancreatic and splenic immune cell infiltration patterns in mouse acute pancreatitis

BACKGROUND

A systemic evaluation of immune cell infiltration patterns in experimental acute pancreatitis (AP) is lacking. Using multi-dimensional flow cytometry, this study profiled infiltrating immune cell types in multiple AP mouse models.

METHODS

Three AP models were generated in C57BL/6 mice via cerulein (CAE) injection, alcohol and palmitoleic acid (EtOH + POA) injection, and alcohol diet feeding and cerulein (EtOH + CAE) injection. Primary pancreatic cells and splenocytes were prepared, and multi-dimensional flow cytometry was performed and analyzed by manual gating and computerized PhenoGraph, followed by visualization with t-distributed stochastic neighbor embedding (t-SNE).

RESULTS

CAE treatment induced a time-dependent increase of major innate immune cells and a decrease of follicular B cells, and TCD4+ cells and the subtypes in the pancreas, whereas elicited a reversed pattern in the spleen. EtOH + POA treatment resulted in weaker effects than CAE treatment. EtOH feeding enhanced CAE-induced amylase secretion, but unexpectedly attenuated CAE-induced immune cell regulation. In comparison with manual gating analysis, computerized analysis demonstrated a remarkable time efficiency and reproducibility on the innate immune cells and B cells.

CONCLUSIONS

The reverse pattern of increased innate and decreased adaptive immune cells was consistent in the pancreas in CAE and EtOH + POA treatments. Alcohol feeding opposed the CAE effect on immune cell regulation. Together, the immune profiling approach utilized in this study provides a better understanding of overall immune responses in AP, which may facilitate the identification of intervention windows and new therapeutic strategies. Computerized analysis is superior to manual gating by dramatically reducing analysis time.

A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry

The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single-cell data in a high-throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data. We introduce the reader to several keystone machine learning-based analytic approaches with an emphasis on defining key terms and introducing a conceptual framework for making translational or clinically relevant discoveries. The target audience consists of cancer cell biologists and physician-scientists interested in applying these tools to their own data, but who may have limited training in bioinformatics. © 2020 International Society for Advancement of Cytometry.

MicroRNA miR-155 is required for expansion of regulatory T cells to mediate robust pregnancy tolerance in mice

The immune-regulatory microRNA miR-155 is reduced in recurrent miscarriage, suggesting that miR-155 contributes to immune tolerance in pregnancy. Here we show miR-155 is induced in the uterine mucosa and draining lymph nodes (dLN) during the female immune response to male seminal fluid alloantigens. Mice with null mutation in miR-155 (miR-155^-/-) exhibited a reduced CD4⁺ T cell response after mating, with a disproportionate loss of CD25+FOXP3+ Treg cells. miR-155 deficiency impaired expansion of both peripheral and thymic Treg cells, distinguished by neuropilin-1 (NRP1), and fewer Treg cells expressed Ki67 proliferation marker and suppressive function marker CTLA4. Altered Treg phenotype distribution in miR-155^-/- mice was confirmed by t-distributed neighbor embedding (tSNE) analysis. Fewer dendritic cells (DCs) and macrophages trafficked to the dLN of miR-155^-/- mice, associated with lower CCR7 on DCs, and reduced uterine Ccl19 expression, implicating compromised antigen presentation in the stunted Treg cell response. miR-155^-/- mice exhibited elevated susceptibility to inflammation-induced fetal loss and fetal growth restriction compared with miR-155^+/+ controls, but outcomes were restored by transfer of wild-type Tregs. Thus miR-155 is a key regulator of immune adaptation to pregnancy and is necessary for sufficient Tregs to achieve robust pregnancy tolerance and protect against fetal loss.

RXRs control serous macrophage neonatal expansion and identity and contribute to ovarian cancer progression

Tissue-resident macrophages (TRMs) populate all tissues and play key roles in homeostasis, immunity and repair. TRMs express a molecular program that is mostly shaped by tissue cues. However, TRM identity and the mechanisms that maintain TRMs in tissues remain poorly understood. We recently found that serous-cavity TRMs (LPMs) are highly enriched in RXR transcripts and RXR-response elements. Here, we show that RXRs control mouse serous-macrophage identity by regulating chromatin accessibility and the transcriptional regulation of canonical macrophage genes. RXR deficiency impairs neonatal expansion of the LPM pool and reduces the survival of adult LPMs through excess lipid accumulation. We also find that peritoneal LPMs infiltrate early ovarian tumours and that RXR deletion diminishes LPM accumulation in tumours and strongly reduces ovarian tumour progression in mice. Our study reveals that RXR signalling controls the maintenance of the serous macrophage pool and that targeting peritoneal LPMs may improve ovarian cancer outcomes.

Macrophages can differentiate to perform homeostatic tissue-specific functions. Here the authors show that RXR signalling is critical for large peritoneal macrophage (LPM) expansion during neonatal life and LPM lipid metabolism and survival during adult homeostasis, and that ovarian cancer growth relies on RXR-dependent LPMs. 

A Systematic Study on Transit Time and Its Impact on Accuracy of Concentration Measured by Microfluidic Devices

Gating or threshold selection is very important in analyzing data from a microflow cytometer, which is especially critical in analyzing weak signals from particles/cells with small sizes. It has been reported that using the amplitude gating alone may result in false positive events in analyzing data with a poor signal-to-noise ratio. Transit time (τ) can be set as a gating threshold along with side-scattered light or fluorescent light signals in the detection of particles/cells using a microflow cytometer. In this study, transit time of microspheres was studied systematically when the microspheres passed through a laser beam in a microflow cytometer and side-scattered light was detected. A clear linear relationship between the inverse of the average transit time and total flow rate was found. Transit time was used as another gate (other than the amplitude of side-scattering signals) to distinguish real scattering signals from noise. It was shown that the relative difference of the measured microsphere concentration can be reduced significantly from the range of 3.43%-8.77% to the range of 8.42%-111.76% by employing both amplitude and transit time as gates in analysis of collected scattering data. By using optimized transit time and amplitude gate thresholds, a good correlation with the traditional hemocytometer-based particle counting was achieved (R² > 0.94). The obtained results suggest that the transit time could be used as another gate together with the amplitude gate to improve measurement accuracy of particle/cell concentration for microfluidic devices.