BioC 2026 - Cytometry in R

Cytometry in R: A free weekly course for flow cytometrist with no-to-little coding experience

David Rach1,2, Natarajan Ayithan2, Xiaoxuan Fan2

1 Molecular Microbiology and Immunology Graduate Program, University of Maryland School of Medicine, Baltimore, USA 2 Flow Cytometry Shared Resource, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore, USA

Within Bioconductor there are 72 packages that list flow cytometry in their BiocViews, collectively permitting analysis of conventional, spectral and mass cytometry data. However, the majority of the packages are underutilized, with only 15 surpassing 4000 yearly downloads. A contributing factor to this is that traditionaly, flow cytometry analysis is carried out using commercial software (ex. FlowJo and FCSExpress), with a graphical-user interface being used to draw gates around a cell population of interest on 2-D plots. Cells falling within this gate are filtered for, additional gates are subsequently drawn, resulting in a gating hierarchy enabling the isolation of a cell population of interest for statistical analysis.

With the emergence of spectral flow cytometry in the last decade, which is capable of profiling 20-50 markers on millions of cells within a few minutes, the resulting datasets are growing increasingly complex. Semi-supervised and unsupervised analytical methods (often implemented as Bioconductor packages) will need to be used in the analysis.

For most flow cytometrist, with no-to-limited coding skills, this presents a barrier to entry. While learning resources exist for those with intermediate coding skills (such as individual package vignetters, workflows, and the odd workshop recording), these are not aimed at beginners. Consequently, many self-study attempts end in frustration.

To address this urgent community need (and reduce the barrier to entry), starting in February 2026 we have been offering a free weekly “Cytometry in R” course, offered both in-person and online, aimed at flow cytometrists with no coding experience. The community response was outstanding, with over 1993 worldwide participants filling out the interest form, 529 participants creating new GitHub accounts and forking the course repository, and between 300-500 distinct viewers per weekly topic.

This course covers one topic per week, with multiple livestream offerings to accomodate different timezones. Recordings are available via our YouTube channel (https://www.youtube.com/@CytometryInR). The course is run out of a GitHub repository (https://github.com/UMGCCCFCSR/CytometryInR), with all course materials, code and datasets being offered under CC-BY-SA and AGPL3-0 licenses. This permits hosting the course website (built using Quarto) as a GitHub page, and utilization of the Discussion page as a community forum to field beginner questions ranging from installation errors, missed library calls, etc.

In addition to teaching R fundamentals to beginners within a cytometry-focused context, we also reinforce how to use Git for version control, Quarto for reproducible documentation, and other good coding practices.

The course is scheduled to continue for 30 weeks, gradually covering intermediate and advanced content (https://umgcccfcsr.github.io/CytometryInR/Schedule). All recordings and course materials will remain available after the course concludes, providing a framework that future self-learners can utilize to make their learning journeys smoother than the ones we experienced when first getting started.

We highlight what has worked, things we wish we had done differently, and unappreciated elements encountered while reducing barriers to entry to a community that would “rather die than ever touch a command line”. We hope the resources made available through the course enable wider utilization of Bioconductor flow cytometry packages, and inspire next generation of package maintainers and developers.

Cyto 2026 - Flow Awarenesss

Being Everything, Everywhere, All at Once: Open-Source Automation for Situational Awareness in SRLs

David Rach1,2, Natarajan Ayithan2, Xiaoxuan Fan2

Operating a flow cytometry shared resource laboratory (SRL) is the art of daily handling issues as they arise, while staying situationally aware enough to avoid a complete meltdown. Given that time is a precious commodity, tools that can provide timely situational awareness of impending issues and thus permit a staged intervention are highly desirable. With the current fiscal environment, free and open-source software tools could assist these efforts, but need to not impose significant additional burden on the staff.

Over the last 18 months, we have sequentially designed and implemented enhanced monitoring tools for all the instruments at our core, using open-source software (R, Python and Rust). These have utilized existing computational resources (as well as computers repurposed following Windows 10 end-of-life) with automation scheduled for off-peak hours to survey for a range of issues across all acquired experiment and .fcs files.

These tools have collectively enabled us to monitor user/laboratory instrument usage over time, track cancellations and altered reservations, build a database of markers and fluorophores by users, and determine their panels respective stain indexes and unmixing-dependent spreading (UDS) hotspots. The system spot checks unmixing controls to identify multiple autofluorescences and tandem fluorophore degradation, with a similar process monitoring for debris build up in the fluidic lines. Additionally, the system reports computer storage space occupancy by user, processes the cytometer logs to classify and record the frequency of returned errors, visualizes instrument QC metrics, as well as tracks cell sorter specific metrics by nozzle.

This framework has expanded our situational awareness beyond what any individual vendor system can provide. It has allowed staff to intervene proactively—reaching out to users before substantial time, samples, and reagents are wasted—at a scale that would have seemed implausible only a few years ago. By standardizing oversight, reducing operator-dependent variability, and documenting performance trends, the system strengthens rigor and reproducibility across SRL workflows.

We emphasize the piecemeal nature of our implementation, highlight what worked well and offer lessons learned from our experience in pursuit to orchestrate situational awareness in an SRL environment.

Cyto 2026 - Alpha Beta

A semi-supervised pipeline for a comprehensive and scalable analysis of immune heterogeneity in human samples

David Rach1,2, Nginache Nampota-Nkomba3,4, Godfrey Mvula3, Felix A. Mkandawire3, Osward M. Nyirenda3, Winter A. Okoth5,6, Daniela Franco4, Andrea G. Buchwald5, Franklin R. Toapanta5, Marcelo B. Sztein5, Miriam K. Laufer5, Kirsten E. Lyke5, Cristiana Cairo7.

1Molecular Microbiology and Immunology Graduate Program, University of Maryland School of Medicine, Baltimore, MD, United States 2Flow Cytometry Shared Resource, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore, MD, United States 3Blantyre Malaria Project, Kamuzu University of Health Sciences, Blantyre, Malawi 4Graduate Program in Life Sciences, University of Maryland School of Medicine, Baltimore, MD, United States 5Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, United States 6Geographic Medicine Division, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States 7Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, United States

Spectral flow cytometry (SFC) holds enormous potential for immune profiling in both breadth and depth. For studies of early life human immunology, where the quantity of biospecimen is limited, SFC may allow for a more thorough interrogation of immune responses following immunization or infection.

The increased number of fluorophores does come with increased uncertainty, affecting the estimation of marker abundance during unmixing. Factors such as additional or varied autofluorescence, non-specific binding or tandem degradation of fluorophores, and increased unmixing-dependent spread due to co-expression of markers on rare subsets, introduce uncertainty when analyzing datasets. Developing systematic bioinformatic methods to distinguish heterogeneity from underlying technical artifacts is critical to the production of interpretable, accurate data using SFC.

Additionally, analysis of complex SFC datasets remains a challenge. Manual gating of pre-determined cell populations of interest risks introducing bias by defaulting to past immunologic assumptions and by design narrows our ability to detect changes in other cell subsets. On the other hand, while existing unsupervised methods may capture a broader picture, it may be difficult to discern findings with biologic relevance.

Studying a cohort of Malawian infants with well-characterized prenatal HIV-exposure, we demonstrate a systematic method of analysis suitable for rare subsets. The cohort consisted of three groups of infants born to women with: A) ART-treated HIV infection and undetectable viral load since before conception and through pregnancy (HEU-lo); B) HIV infection diagnosed and treated at mid-gestation or later, with high viral load at enrollment (HEU-hi); C) no HIV infection (HU). Cryopreserved cord blood mononuclear cells and longitudinal peripheral blood mononuclear cells collected at 4 and 9-months following routine childhood immunizations were thawed, rested for 6 hours, and stimulated in the presence of purified protein derivative (PPD) or Staphylococcal enterotoxin B (SEB). Cells were stained with a 32-color SFC panel to characterize phenotype, activation-induced markers and cytokine production with same day acquisition using a 5-laser Cytek Aurora.

Following acquisition, we carried out a semi-supervised analysis with a custom pipeline that we implemented in R and Rust. Briefly, we performed quality assurance of unmixing controls using the Luciernaga and PeacoQC R packages, replacing problematic single-colors and/or autofluorescence controls as appropriate before unmixing. Samples were biexponentially transformed and automated gating was implemented using the openCyto and flowMagic R packages. Using the interactive Shiny App in our R package Coereba, the calculated split-points between positive and negative events were visualized for each marker and manually adjusted as needed. Identity columns for the marker expression of individual cells were then generated and appended to the .fcs files.

For select cell populations, all events were concatenated, normalized with CytoNorm v2, clustered with flowSOM, and visualized with PaCMAP. Identity data associated with cells across clusters was retrieved, allowing a comparison between algorithm and manual gating performance. In parallel, cells sorted into clusters on the basis of shared marker identity were analyzed to compare their abundance between exposure groups using existing Bioconductor workflows. This mixed approach allowed us to examine the immune response across timepoints, treatment conditions and exposure groups, enabling an exhaustive, comprehensive and scalable characterization of individual heterogeneity.

Cyto 2026 - Cytometry In R

Cytometry in R: A free weekly course for coding beginners

David Rach1,2, Natarajan Ayithan2, Xiaoxuan Fan2 1 Molecular Microbiology and Immunology Graduate Program, University of Maryland School of Medicine, Baltimore, USA 2 Flow Cytometry Shared Resource, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore, USA

As spectral flow cytometry panels continue to expand in complexity, comprehensive analysis of the resulting high-dimensional datasets becomes increasingly challenging. The identification of previously uncharacterized cell populations within this multidimensional space remains a significant obstacle. To address this, both semi-supervised and unsupervised analytical approaches are required to enable unbiased discovery of cell subsets. Many of these advanced computational methods and algorithmic frameworks are implemented and readily accessible through R-based packages.

While cytometry enthusiasts often express interests in learning R, significant barriers to learning exist. For those without access to institutional computational cytometry expertise, the question is often where to start. As the few existing online resources are primarily aimed at those with intermediate bioinformatic skills, self-study attempts often end in frustration due to lack of beginner suitable materials and troubleshooting support.

To address this community need, we developed free weekly ‘Cytometry in R’ course, designed for researchers with prior flow cytometry experience but no coding background. The course focuses on one topic each week, and is offered both in-person and via YouTube livestream, with recordings made available immediately after (https://www.youtube.com/@CytometryInR). All code and course materials are accessible via a GitHub repository(https://umgcccfcsr.github.io/CytometryInR/). In our commitment to open-science, all teaching materials are licensed under CC-BY-SA, and code is distributed under the copyleft AGPL3-0 license. A moderated discussion forum further supports participants by enabling troubleshooting, Q&A, and continued exploration of course topics.

Launched in February 2026, the course has generated substantial community interest, with over 1988 individuals completing the interest form. Using various metrics (Google Analytics, YouTube views, GitHub fork updates) around 500 participants have started the course, with between 250 and 350 active weekly participants. Overall reception has been highly positive, and the course continues to evolve in response to participant feedback and ongoing evaluation.

The course is comprehensive, with over 30 weeks of topics planned, intended to provide beginners solid foundations in R before moving on to intermediate and advanced cytometry topics as their coding skills and troubleshooting expertise develops. As all recordings and teaching materials will remain freely available after course completion, we hope to significantly reduce the existing barriers to analyzing cytometry data in R, permitting anyone who starts their own self-study journey in the future has a smoother journey than the one we ourselves experienced when first getting started.

ABRF 2026 - Complex Data Analysis

As flow cytometry advances with new technologies and techniques, the data generated becomes increasingly rich and complex. This session features a panel of three speakers who are tackling these modern datasets head-on. The presentations are designed not only to inform attendees about the current state of the field but to inspire them to engage with these powerful tools. By breaking down the “art” of dealing with complex data, this session aims to strip away the intimidation factor and make high-dimensional analysis accessible to the wider flow community.

Cyto 2025 - Autofluorescence - P267

“Are these autofluorescences in the room with us right now?” Quantifying impact of autofluorescence variation on unmixing

David Rach1, Kirsten E. Lyke2, Cristiana Cairo3

1 Molecular Microbiology and Immunology Graduate Program, University of Maryland School of Medicine, Baltimore, USA 2 Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, USA 3 Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, United States.

Proper unmixing controls (both single-color and unstained) are critical for successful resolution of similar fluorophores in spectral flow cytometry (SFC). When using cells in the place of beads, the unstained unmixing controls are particularly important, given that they enable subtraction of the autofluorescence background present within single-color unmixing controls, and potentially serve as an additional fluorophore.

Autofluorescence, especially in context of human cord and peripheral mononuclear cells (CBMCs and PBMCs), is often treated as a single fluorophore, primarily differing between cell populations in brightness. However, the sources of autofluorescence within a cell may vary and are affected by cell activation, cryopreservation, and fixation during processing. Thus, at the single cell level, autofluorescence signatures may be slightly different. The point at which variation in autofluorescence at the individual cell level goes from being negligible to impacting the resolution of a complex panel is still being addressed. If multiple autofluorescence signatures are present in a population but unaccounted for in the unmixing matrix, uncertainty is introduced, reducing the ability to resolve other fluorophores. This is particularly problematic within large spectral panels with closely related fluorophores and complex marker co-expression patterns. However, the addition of multiple highly similar autofluorescence signatures to an unmixing matrix can increase the complexity with further loss of resolution.

We set out to quantitatively interrogate at which point differences in autofluorescence signatures within human mononuclear cells begin to impact panel resolution. Using our R package Luciernaga, we profiled autofluorescence signatures in more than 150 cryopreserved CBMC and PBMC specimens, treated with different activation conditions and with different fixatives. For each .fcs file, we quantified the normalized signatures of individual cells, grouped and enumerated cells based on shared signatures, and visualized the data across specimens and treatments. Since we had samples stained with complex panels matching many of the unstained controls, we used autofluorescence signatures isolated from the unstained controls to iteratively unmix the samples in R, employing ordinary least squares to evaluate the effect these signatures had upon unmixing.

We found that the majority of the autofluorescence signatures within human mononuclear cells acquired on a 5-laser Cytek Aurora® share a common primary peak (typically on detector V7). Variation in the relative height of the second and third peak (typically UV7 and B3, respectively) was noted between CBMC and PBMC, as well as treatment conditions. A degree of variation in the height of the second and third peak was tolerated without impacting the unmixing. This pattern held true for rare “variant” signatures that did not have a primary peak on V7, as long as they shared the same primary peak. These variant signatures were different enough to cause unmixing errors when present in >1% of PBMC, explaining unmixing issues we previously encountered.

Our work highlights variation in autofluorescence signatures within human CBMC and PBMC cells activated with different stimuli, and established thresholds at which we observe impacts on the effect that unmixing controls had on resolving complex SFC panels. We provide a method by which shared variant autofluorescence signatures can be isolated and highlight the importance of collecting sufficient unstained cells to profile rarer variation in autofluorescence that might otherwise be missed.

Cyto 2025 - Single Colors - P299

“Well, how bright does it need to be?”: Investigating the interplay of fluorescent signature and brightness in single-color unmixing controls.

David Rach1, Kirsten E. Lyke2, Cristiana Cairo3

Spectral flow cytometry (SFC), with its capacity to resolve similar fluorophores and ability to rapidly acquire large number of events, enables more comprehensive phenotypic and functional analyses than conventional flow cytometry. Unmixing controls (both single-color and unstained) are critical for proper unmixing of high-dimensional panels, as their fluorescence signatures provision the reference matrix. Discrepancies between the fluorescence signatures of unmixing controls and full-stained samples add uncertainty to the unmixing calculations and can lead to loss of resolution for individual fluorophores, (particularly in large panels with broader co-expression of markers) producing batch effects.

These batch effects can affect performance, increase complexity, and impact reproducibility of unsupervised analysis methods that rely on median fluorescent intensity (MFI) for clustering, dimensionality visualization and normalization. While previous efforts at quality control have focused on factors related to instruments and/or full-stained samples, few tools exist to evaluate unmixing controls despite their critical importance. At the same time, the criteria for delineating a good unmixing control and re-using previous controls without affecting the unmixing remain loosely defined.

We therefore set out to quantitatively assess how variation in the fluorescence signature and/or brightness of controls impacts unmixing of full-stained samples. To this end, we have been working on an R package called Luciernaga, which includes a collection of tools for quality control and fluorescence signature profiling of unmixing controls. Leveraging its ability to characterize normalized fluorescence signatures for individual cells, we grouped and quantified, in every unmixing control, cells with similar signatures across 20 experiments. In the process, we identified “variant” signatures indicative of tandem degradation and non-specific binding of decoupled fluorophores associated with specific unmixing issues. We then grouped cells with shared variant signatures in each unmixing control and used them to generate a .fcs file containing a single variant signature. Keeping all unmixing controls but one constant, we swapped in a variant signature at a time, performing iterative unmixing in R with ordinary least squares. This process allowed us to characterize how variations in fluorescence signature, brightness, or both factors impact the unmixing of the full-stained sample. Our work builds on fluorescence signature, brightness, or both factors impact the unmixing of the full-stained sample.

Our work builds on the existing guidelines for good unmixing controls, while providing mechanistic explanations for each. We also highlight advantages of profiling control signatures before performing unmixing as a means to mitigate unmixing issues.

Cyto 2025 - MFI Drift After QC - P310

““Wait, when was QC last run???” Evaluating MFI drift after morning QC and its impact on unmixing.”

David Rach1, Mikayla Trainor2, Natarajan Ayithan2, Xiaoxuan Fan2

At our core, quality control (QC) beads are run daily on Cytek Aurora instruments as part of the instrument startup process. Based on the beads initial observed MFI values for each detector, the SpectroFlo software adjusts the gains and laser settings of the instrument to ensure that the after-QC MFI values match lot-specific thresholds. This accounts for instrumental changes, allowing specimens acquired on different days to be comparable, and reducing the frequency of MFI-based batch effects in large spectral flow cytometry panels. We recently implemented a website to monitor daily QC for our instruments at our core https://umgccfcss.github.io/InstrumentQC/, visualizing longitudinal changes in gain, RCV, and bead MFI values before and after daily QC. In the process, we observed various detectors for which MFI values pre-QC were consistently different from the MFI values observed after QC the day before. While MFI was reset to baseline at the following QC, we were curious whether these observed MFI drifts would have already occurred by the evening before, or were a result of the instrument shutdown. Additionally, we wanted to evaluate whether these drifts were sufficient to impact unmixing when samples were acquired in the evening compared to shortly after morning QC.

To evaluate this, we acquired 5000 SpectroFlo QC beads as fcs files, i) before morning QC, ii) after morning QC, iii) before evening QC, and iv) after evening QC. These samples were acquired on a 3, 4, and 5-laser Aurora over a several month period, for both the 2005 and 2006 SpectroFlo QC bead lots. For analysis, acquired FCS files were imported to R, singlet beads gated, and gate placement validated using the flowWorkspace, openCyto and Luciernaga R packages. From the gated events, median MFI and RCV values were calculated for each detector, and voltage/gain metadata for individual .fcs files was retrieved using the Luciernaga package. We then visualized this data in R using various tidyverse packages. We observed that for most detectors, there were limited changes in MFI values between the After Morning QC and Before Evening QC timepoints. However, we noted consistent and significant shifts in MFI for a few detectors by the time of evening QC, notably the YG2, YG3, and R1 detectors. To evaluate whether these observed drifts in MFI would have altered normalized fluorescent signature, we simulated the equivalent day-specific adjustment to reference signatures of over 100 fluorophores, plotting the adjusted signatures against the original reference signature. We observed that the drift in the handful of detectors did not significantly alter the normalized fluorescent spectra of the fluorophore, with exception of a few fluorophores where the detector in question landed on the secondary peak of the spectra. When evaluating the signatures by their cosine value, the differences were within range we would anticipate limited impact on unmixing.

Finally using the same approach, we adjusted raw reference controls and full-stained samples acquired following morning QC and imported into SpectroFlo for unmixing. We did not observe any major changes to the unmixing in medium-sized panels. In summary, for the instruments at our core, the majority of detector MFI values remain stable following morning QC. Additionally, for the few detectors that did consistently shift, based on both simulated and experimental data, the observed changes would have minimal impact for most fluorophores signatures and subsequent unmixing. Whether these observed small changes are enough to affect unmixing in large panels (40+) colors remains an area that merits further investigation.

10th International γδ T cell Conference 2023 - P55

Vγ9Vδ2 T cell responses in HIV‑exposed Uninfected (HEU) Infants

David Rach1, Hao-Ting Hsu2, Nginache Nampota3, Godfrey Mvula3, Felix A. Mkandawire3, Osward M. Nyirenda3, Bernadette Hritzo1, Ingrid Peterson4, Franklin R Toapanta4, Marcelo B Sztein4, Miriam Laufer4, Kirsten E. Lyke4, Cristiana Cairo2

1Molecular Microbiology and Immunology Graduate Program, University of Maryland School of Medicine, Baltimore, USA. 2Institute of Human Virology, University of Maryland School of Medicine, Baltimore, USA. 3Blantyre Malaria Project, Kamuzu University of Health Sciences, Blantyre, Malawi. 4Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, USA

Maternal antiretroviral therapy (ART) effectively prevents perinatal infection of infants born to HIV+ women. However, during the first six months of life HIV-exposed, uninfected (HEU) infants exhibit increased infectious morbidity compared to HIV unexposed (HU) infants. Dysfunction of the infant immune system, driven directly or indirectly by prenatal HIV/ART exposure, is thought to contribute to this outcome. Vγ9Vδ2 T (Vδ2) cells, with their ability to rapidly release Th1 cytokines and respond to IL-23, are likely to play a key role against pathogens in early life. Due to elevated inflammation at the fetal-maternal interface, Vδ2 cells, which are sensitive to inflammatory cytokines, may be dysfunctional in HEU infants. This issue, however, has not been investigated. We are analyzing a well-characterized Malawian infant cohort, comparing infants born to women with: A) ART-treated HIV infection, with undetectable viral load since before conception (HEU-lo); B) HIV infection diagnosed and treated at mid-gestation or later, with high viral load at enrollment (HEU-hi); C) no HIV infection (HU). We are employing conventional and spectral flow cytometry for a detailed assessment of cord blood Vδ2 cells.Ex vivo, we observed an increased frequency of Vδ2 cells in cord blood of HEU-hi infants compared to HU infants. Following short polyclonal stimulation, the frequency of Vδ2 cells producing IFNγ, or both INFγ and TNFα was only significantly elevated in HEU-lo infants. TCR-mediated restimulation of Vδ2 cells resulted in lower frequency of Th1 cytokine producing cells and CD107a+ cells in HEU-hi infants after expansion with BCG, but not after expansion with Zoledronate. Results for our Malawian neonates confirm elevated Vδ2 cell frequencies in the cord blood of HEU infants, which we previously observed in a Nigerian cohort.To optimize the use of precious clinical specimens and extend the analyses to the other human innate-like subsets, we designed a 29-color spectral flow cytometry panel that enables a comprehensive profiling of Vδ2 T, MAIT and NKT cells. The direct comparison of conventional and spectral data for Vδ2 cells will help validate the performance of large spectral flow cytometry panels to highlight the impact of HIV prenatal exposure on these rare subsets.

Cyto 2023 - P195

Spectral flow cytometry analysis of Innate-like T cell responses in Malawian HIV-exposed Uninfected (HEU) Infants.

David Rach1, Hao-Ting Hsu2, Nginache Nampota3, Godfrey Mvula3, Felix A. Mkandawire3, Osward M. Nyirenda3, Ingrid Peterson4, Franklin R. Toapanta4, Marcelo B. Sztein4, Miriam Laufer4, Kirsten E. Lyke4, Cristiana Cairo2

1 Molecular Microbiology and Immunology Graduate Program, University of Maryland School of Medicine, Baltimore, USA. 2 Institute of Human Virology, University of Maryland School of Medicine, Baltimore, USA. 3 Blantyre Malaria Project, Kamuzu University of Health Sciences, Blantyre, Malawi. 4 Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, USA

Maternal antiretroviral therapy (ART) effectively prevents perinatal infection of infants born to HIV+ women. However, during the first six months of life HIV-exposed, uninfected (HEU) infants exhibit increased morbidity due to lower respiratory tract and diarrheal infections, compared to HIV unexposed (HU) infants. It is hypothesized that exposure to HIV and/or ART before birth perturbs the developing fetal immune system, which contributes to the increased infectious morbidity seen in HEU infants. The specific immunological mechanisms underlying this clinical outcome are still under investigation. To help understand the contribution of maternal viremia to infant immune perturbation, we are analyzing a cohort of Malawian infants with well-characterized prenatal HIV-exposure. Specifically, we are comparing three groups of infants born to women with: A) ART-treated HIV infection and undetectable viral load since before conception and through pregnancy (HEU-lo); B) HIV infection diagnosed and treated at mid-gestation or later, with high viral load at enrollment (HEU-hi); C) no HIV infection (HU).

Innate-like T cells (ILTs), including Natural Killer T cells (NKTs), Mucosal-associated Invariant T cells (MAITs), and Vg9Vd2 (Vd2) T cells may be perturbed due to elevated inflammation at the fetal maternal interface during maternal HIV infection. ILTs, which are thought to play important roles against pathogens in early life, are activated by microbial metabolites as well as by innate cytokines, mounting Th1-like and cytotoxic responses in the early phase of infection. The effect exerted by in utero HIV exposure on infant ILTs is unknown, as the markers required for their identification are not routinely included in conventional flow cytometry panels, and their low abundance in infant blood makes mass cytometry impractical for their assessment.

To overcome these limitations, we designed a 29-color spectral flow cytometry panel that allows for concomitant assessment of ILT subsets, employing human CD1d and MR1 tetramers to identify NKTs and MAITs, respectively. A preliminary analysis of cord blood specimens from neonates in our Malawian cohort by conventional flow cytometry has shown an increased Vd2 cell frequency, differentiation, and activation in HEU-hi infants, while the frequency of Vd2 cells producing IFNg or both INFg and TNFa upon polyclonal stimulation was significantly elevated in HEU-lo infants. In a distinct African cohort, HEU infants also displayed elevated MAIT frequency at birth. We thus incorporated markers that characterize ILT subset activation, differentiation, and function. The spectral flow cytometry results that we will present will be corroborated by direct comparison to conventional flow cytometry data. The implementation of this optimized spectral panel will allow a comprehensive profiling of these rare subsets in infants and help highlight effects arising in context of HIV prenatal exposure.

ASTMH 2021 - P369

INNATE-LIKE T CELL RESPONSES IN HIV EXPOSED UNINFECTED MALAWIAN INFANTS

David Thomas Rach1, Hao-Ting Hsu2, Nginache Nampota3, Godfrey Mvula3, Franklin R. Toapanta4, Marcelo B. Sztein4, Miriam Laufer4, Kirsten E. Lyke4, Cristiana Cairo2

1 Molecular Microbiology and Immunology Graduate Program, University of Maryland School of Medicine, Baltimore, MD, United States, 2 Institute for Human Virology, University of Maryland School of Medicine, Baltimore, MD, United States, 3 Blantyre Malaria Project, University of Malawi College of Medicine, Blantyre, Malawi, 4 Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, United States

Each year, an estimated 1.5 million HIV+ women give birth. Increased access to antiretroviral therapy (ART) ensures most infants born to HIV+ women do not contract the infection vertically, and thus remain HIV exposed but uninfected (HEU). During their first year of life, HEU infants exhibit increased rates of lower respiratory tract infections and diarrheal diseases, compared to HIV unexposed (HU) infants. It is hypothesized that exposure to HIV (and ART) before birth perturbs the developing fetal immune system by increasing inflammation at the fetal-maternal interface and induces long term effects that contribute to the increased infectious morbidity in HEU infants. The specific immunologic mechanisms behind this clinical outcome remain unclear. Among the subsets that may be perturbed by increased inflammation, innate-like T cells (γδ, MAIT, NKT cells) may play an important role against pathogens in early life. We hypothesize that prenatal HIV exposure results in early life activation of innate-like T cell subsets. Utilizing a well-characterized cohort of Malawianinfants, we compare three groups of infants born to women with: 1) HIV infection but undetectable viremia through pregnancy (HEU-lo); 2) HIV infection diagnosed at mid-gestation or later, with high viral loads (HEUhi); 3) no HIV infection (HU). A preliminary analysis of cord blood Vδ2 T cells suggests that production of Th1 cytokines in response to polyclonal stimulation is increased in HEU neonates. The frequency of TNFα+ Vδ2 T cells is highest in HEU-hi neonates, while the frequency of polyfunctional (IFNγ+TNFα+) Vδ2 cells is highest in HEU-lo neonates. These findings, if confirmed, would suggest that exposure to replicating maternal HIV results in more robust TNFα production, while prolonged exposure to ART in HEU-lo neonates may contribute to the observed differences in Vδ2 cells polyfunctionality. This study provides an opportunity to assess immune perturbation of innate-like subsets in HEU infants, contributing to our understanding of immune responses and mechanisms of increased infectious morbidity in this vulnerable population.