scholarly journals With great power comes great responsibility: high-dimensional spectral flow cytometry to support clinical trials

Bioanalysis ◽  
2021 ◽  
Author(s):  
Megan McCausland ◽  
Yi-Dong Lin ◽  
Tania Nevers ◽  
Christopher Groves ◽  
Vilma Decman

Flow cytometry is a powerful technology used in research, drug development and clinical sample analysis for cell identification and characterization, allowing for the simultaneous interrogation of multiple targets on various cell subsets from limited samples. Recent advancements in instrumentation and fluorochrome availability have resulted in significant increases in the complexity and dimensionality of flow cytometry panels. Though this increase in panel size allows for detection of a broader range of markers and sub-populations, even in restricted biological samples, it also comes with many challenges in panel design, optimization, and downstream data analysis and interpretation. In the current paper we describe the practices we established for development of high-dimensional panels on the Aurora spectral flow cytometer to aid clinical sample analysis.

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3976-3976
Author(s):  
Shahrzad Jalali ◽  
Jose Villasboas ◽  
Jie Shi ◽  
Cole Bothun ◽  
Hyojin Kim ◽  
...  

Myeloid derived suppressor cells (MDSC) are a heterogeneous population of undifferentiated myeloid cells that are expanded and activated in pathological conditions and have the ability to potently suppress T-cell function and thereby contribute to immunosuppression and tumor progression. While there have been studies showing a role for MDSC in a variety of hematological malignancies, no data is available indicating that MDSCs contribute to the tumor progression in Waldenstrom's Macroglobulinemia (WM), an indolent lymphoma characterized by bone marrow (BM) infiltration of lymphoplasmacytic (LPL) cells and increased secretion of monoclonal IgM. In previous work, we have found increased GM-CSF and reduced arginine and cysteine in the BM microenvironment in WM. We hypothesized that this was due to the presence and activity of MDSCs in WM. BM aspirates from WM patients (n=17) were therefore processed to isolate LPL (CD19+/CD138+) cells from the rest of the BM cells (CD19-/CD138-). Sorted (CD19-/CD138-) cells from BM of patients with WM were studied with flow cytometry. Using a sequential gating strategy (lack of lineage markers, low levels of HLADR, CD33+, CD11b+) we identified a population of MDSCs that were then subdivided using CD14 and CD15 expression into total-, monocytic-, or granulocytic- MDSCs (m-MDSC, g-MDSC). We also analyzed unsorted BM cells using cytometry by time-of-flight (CyTOF) in order to further identify and phenotypically characterize the BM MDSC population in a group of WM patients with smoldering (asymptomatic) disease, symptomatic disease, or in remission post-treatment. BM samples from normal subjects were used as a control. Flow cytometry data showed significant higher numbers of MDSC subsets expressing PD-L1 and Arginase1 in WM patients when compared to the normal samples. BM cells from WM patients (n=18) then were compared to controls (n=11), and the absolute number of the total MDSC (p=0.05), m-MDSC (p=0.002), g-MDSC (p=0.02) was increased in WM specimens. When MDSCs from WM or normal monocytes from healthy controls were co-cultured with activated T-cells, the proliferation of activated T-cells in the presence of MDSCs from WM patients was impaired compared to controls, confirming the suppressive role of MDSCs. We then performed high dimensional analysis of the total BM MDSC cells using t-SNEand identified phenotypically distinct MDSC cell populations in the BM that were differentially present when healthy controls were compared to patients with smoldering WM or those with WM needing treatment. Specifically, WM patients needing treatment had increased numbers of a distinct MDSC population that was highly positive for CD163, and CD138. Moreover, conventional markers denoting m-MDSC and g-MDSC, such as CD14 and CD15, were highly expressed in all populations and their pattern of expression did not specifically define the MDSC subtypes, indicating that high dimensional phenotyping further details the MDSC sub-compartments beyond the conventional categorization of MDSC using conventional cytometry. In summary, we find that MDSCs are increased in the BM of WM patients compared to controls. MDSCs expressing CD163 and CD138 increase when WM patients become symptomatic and require therapy. Furthermore, MDSCs in the BM of WM patients suppress T-cell function and likely contribute to progression of the disease. MDSCs in the BM therefore present a therapeutic target that should be explored in WM patients. Disclosures Ansell: Bristol Myers Squibb: Other: research funding for clinical trials; Merck: Other: research funding for clinical trials; AI Therapeutics: Other: research funding for clinical trials; Affimed: Other: research funding for clinical trials; Takeda: Other: research funding for clinical trials; Pfizer: Other: research funding for clinical trials; Regeneron: Other: research funding for clinical trials; Seattle Genetics: Other: research funding for clinical trials.


2019 ◽  
Author(s):  
L Ferrer-Font ◽  
C Pellefigues ◽  
JU Mayer ◽  
S Small ◽  
MC Jaimes ◽  
...  

ABSTRACTTechnological advances in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system has led to the development of large 20+ flow cytometry panels. Yet, as panel complexity and size increases, so does the difficulty involved in designing a high-quality panel, accessing the instrumentation capable of accommodating large numbers of parameters, and in analysing such high-dimensional data.A recent advancement is spectral flow cytometry, which in contrast to conventional flow cytometry distinguishes the full emission spectrum of each fluorochrome across all lasers, rather than identifying only the peak of emission. Fluorochromes with a similar emission maximum but distinct off-peak signatures can therefore be accommodated within the same flow cytometry panel, allowing greater flexibility in terms of panel design and fluorophore detection.Here, we highlight the specific characteristics regarding spectral flow cytometry and aim to guide users through the process of building, designing and optimising high-dimensional spectral flow cytometry panels using a comprehensive step-by-step protocol. Special considerations are also given for using highly-overlapping dyes and a logical selection process an optimal marker-fluorophore assignment is provided.


2020 ◽  
Vol 97 (8) ◽  
pp. 824-831 ◽  
Author(s):  
Laura Ferrer‐Font ◽  
Johannes U. Mayer ◽  
Samuel Old ◽  
Ian F. Hermans ◽  
Jonathan Irish ◽  
...  

2020 ◽  
Vol 92 (1) ◽  
Author(s):  
Laura Ferrer‐Font ◽  
Christophe Pellefigues ◽  
Johannes U. Mayer ◽  
Sam J. Small ◽  
Maria C. Jaimes ◽  
...  

2021 ◽  
Vol 12 ◽  
Author(s):  
Hannah den Braanker ◽  
Margot Bongenaar ◽  
Erik Lubberts

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.


2021 ◽  
Vol 9 (4) ◽  
pp. e002231
Author(s):  
Romain Banchereau ◽  
Avantika S. Chitre ◽  
Alexis Scherl ◽  
Thomas D. Wu ◽  
Namrata S. Patil ◽  
...  

BackgroundCD8+ tissue-resident memory T (TRM) cells, marked by CD103 (ITGAE) expression, are thought to actively suppress cancer progression, leading to the hypothesis that their presence in tumors may predict response to immunotherapy.MethodsHere, we test this by combining high-dimensional single-cell modalities with bulk tumor transcriptomics from 1868 patients enrolled in lung and bladder cancer clinical trials of atezolizumab (anti-programmed cell death ligand 1 (PD-L1)).ResultsITGAE was identified as the most significantly upregulated gene in inflamed tumors. Tumor CD103+ CD8+ TRM cells exhibited a complex phenotype defined by the expression of checkpoint regulators, cytotoxic proteins, and increased clonal expansion.ConclusionsOur analyses indeed demonstrate that the presence of CD103+ CD8+ TRM cells, quantified by tracking intratumoral CD103 expression, can predict treatment outcome, suggesting that patients who respond to PD-1/PD-L1 blockade are those who exhibit an ongoing antitumor T-cell response.


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