Flow Cytometric Data Research Articles

BCL11B Is Involved in Stress-Induced Differentiation of Keratinocytes and Has A Potential Role in Psoriasis Pathogenesis.

Psoriasis is a common, auto-immune skin disease characterized by abnormal proliferation and differentiation of keratinocytes. Studies revealed the role of stress stimulators in the pathogenesis of psoriasis. Oxidative stress and heat shock are two important stress factors tuning differentiation and proliferation of keratinocytes, regarding to psoriasis disease. BCL11B is a transcription factor with critical role in embryonic keratinocyte differentiation and proliferation. Given this, in keratinocytes we have investigated potential role of BCL11B in stress-induced differentiation. Furthermore, we searched for a potential intercommunication between BCL11B expression and psoriasis-related keratinocyte stress factors. In this experimental study, data sets of psoriatic and healthy skin samples were downloaded in silico and BCL11B was chosen as a potential transcription factor to analyze. Next, a synchronized in vitro model was designed for keratinocyte proliferation and differentiation. Oxidative stress and heat shock treatments were employed on HaCaT keratinocytes in culture, and BCL11B expression level was measured. Cell proliferation rate and differentiation were analyzed by synchronized procedure test. Flow cytometry was done to analyze cell cycle alterations due to the oxidative stress. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) data revealed a significant upregulation of BCL11B expression in keratinocytes, by 24 hours after initiating differentiation. However, it was followed by a significant down-regulation in almost all the experiments, including the synchronized model. Flow cytometer data demonstrated a G1 cell cycle arrest in the treated cells. Results indicated a remarkable role of BCL11B in differentiation and proliferation of HaCaT keratinocytes. This data along with the results of flow cytometer suggested a probable role for BCL11B in stress-induced differentiation, which is similar to what is happening during initiation and progression of normal differentiation.

A new automatic gating algorithm replicates expert manual gating of immune cell subsets

Abstract Machine learning is increasingly used for the analysis of flow cytometric immunophenotyping data. Many analysis pipelines leverage unsupervised approaches such as dimensionality reduction or clustering, which may lead to populations characterized differently from expert definition. Here we show a new, supervised algorithm for population identification that faithfully replicates the expert manual gating strategy. Peripheral blood samples were drawn from 41 different donors, stained with a dry, unitized B cell panel and acquired on two cytometer units. A manual gating strategy was established, and an automatic model trained on a subset of samples (n=31 donors, split into training, validation and test set) from this study. The performance of the automatic model was evaluated on the 10 unused samples. The automatic identification of populations with differences in median abundance (lymphocytes, CD19+ B-cells and CD19+ CD10+ cells) showed a high level of correlation and lower variability compared to manual gating. The supervised approach presented here retains the gating hierarchy, in contrast to the unsupervised clustering performed by FlowSOM, which splits cells into exclusive metaclusters, leading to metacluster assignments significantly different from expert gating. By automating the identification of populations, assay reproducibility can be increased by removing inter-operator variability. We show that a supervised machine learning tool that supports user-defined panels and gating strategies can reproduce expert analysis, reduce variability of flow cytometry data analysis, and can provide better fit-for-purpose compared to an unsupervised clustering approach.

Impact of the LPS from Cyanobacteria Brasilonema sp.on Lung Inflammation and Glucocorticoid Expression

Abstract Inhaled glucocorticoids (GCs) are the first-line treatment for asthma. However, patients with asthma characterized by recruitment of neutrophils show poor sensitivity to GC therapy. A potential etiology of this GC-resistant asthma is aerosolized cyanobacterial lipopolysaccharide (LPS) from cyanobacterial blooms. Previously, we demonstrated that inhalation of LPS from Geitlerinema sp. of cyanobacteria induces neutrophilic lung infiltration in mice. Our current data shows that the LPS from another cyanobacteria, Brasilonema sp., also induces lung inflammation after inhalation. Flow cytometric data demonstrate that Brasilonema sp. LPS induces neutrophil infiltration and ELISA identified increases in pro-inflammatory cytokine levels in bronchoalveolar lung fluid after Brasilonema sp. LPS exposure. Furthermore, IHC data demonstrate that mice exposed to Brasilonema sp. LPS show an increased level of lung epithelial GCRβ, a glucocorticoid receptor (GR) analog that inhibits GC activity. Conversely, expression of GCRα, the GR isoform responsible for glucocorticoid sensitivity, remains unchanged. These data help to elucidate the mechanisms of Brasilonema sp. LPS-induced lung-inflammation and potential glucocorticoid resistance, while highlighting the importance of the toxin in promoting human disease near cyanobacterial blooms. This work is supported by the Kenneth A. Suarez Fellowship and Intramural Funds at Midwestern University.

OA26 Characterisation of a basket trial population for arthritis associated with ANA-positive RMDs

Abstract Background/Aims Musculoskeletal inflammation is the most common reason for the inclusion of patients in SLE trials or biologic use. It is also common across other ANA-associated diseases, but these patients do not have access to the same therapies as SLE patients despite a seemingly similar problem. Our aims were to test whether (i) patient-reported impact and (ii) immunological profile of MSK inflammation are equivalent across ANA-associated diseases; (iii) identify features that stratify ANA-associated arthritis; (iv) explore the criterion validity of BILAG-MSK in other ANA-associated arthritis. Methods We used cross-sectional analysis of patient-reported outcome measures, antibody, gene expression and flow cytometric data from a study of mixed ANA-associated diseases (DEFINITION). The clinical and immunological implications of legacy diagnosis membership were analysed by comparing QoL measures (SF36, EQ5D-5L, Patient VAS, ICECAP-A, FACIT-F, WPAI and ICECAP-A), gene expression scores (2 IFN Scores, neutrophil, plasmablasts, myeloid and inflammation scores) and flow cytometry using Kruskal Wallis tests. The association between the BILAG MSK domain and physician arthritis VAS was tested for SLE and non-SLE patients. Results Of 300 patients, 215 had current or prior joint or tendon inflammation. Only FACIT-F and EQ5DL-5L showed trends to difference between diseases, being worst in the MCTD group. No other clinical or immunological variable differed between groups (Table 1). However, significant associations were found for non-disease-specific variables; worse EQ5D-5l index scores were associated with chromatin antibody positivity (T-Test, p < 0.001) and non-European ancestry (T-Test, p < 0.05). There was a significant association between BILAG-MSK and physician arthritis VAS in both SLE (Kruskal Wallis Chi-Sq=56.8, p < 0.0001), and non-SLE (Kruskal Wallis Chi-Sq=17.468, p < 0.001) patients. Conclusion Patients with ANA-associated arthritis appear clinically homogenous across legacy diagnoses so performing trials in a basket population appears appropriate and may be more clinically homogenous than populations currently included in SLE trials who have mixed organ involvement. Furthermore, such a basket trial could benefit from an organ-specific outcome measure, which our preliminary analysis of BILAG-MSK suggests would be valid. Predictors of worse symptoms are not specific to individual diseases, including autoantibody profile and ancestry. Disclosure J. Arnold: None. L. Carter: Consultancies; UCB. S. Hassan: None. K. Dutton: None. Z. Wigston: None. S. Dass: Consultancies; Roche, Abbvie, UCB and Chugai. Honoraria; Roche, Abbvie, UCB and Chugai. M. Md Yusof: Consultancies; Aurinia and, UCB. E. Vital: Consultancies; Roche, GSK, AstraZeneca, Aurinia, Lilly, UCB and Novartis. Grants/research support; Roche, AstraZeneca and Sandoz.

Retracted] MicroRNA‑24 upregulation inhibits proliferation, metastasis and induces apoptosis in bladder cancer cells by targeting CARMA3.

Following the publication of this paper, it was drawn to the Editor's attention by a concerned reader that the cell‑cycle assay data shown in Fig.2D, and certain of the flow cytometric data shown in Fig.2E, on p.1354 had already been submitted in different form in papers written by different authors at different research institutes. Moreover, a pair of data panels shown for the Transwell assay experiments in Fig.4A were overlapping, such that data purportedly showing the results of differently performed experiments were likely to have been derived from the same original source. Owing to the fact that the contentious data in the above article had already been submitted for publication prior to its submission to International Journal of Oncology, and due to an overall lack of confidence in the data, the Editor has decided that this paper should be retracted from the Journal. After having been in contact with the authors, they accepted the decision to retract the paper. The Editor apologizes to the readership for any inconvenience caused. [International Journal of Oncology 47: 1351‑1360, 2015; DOI: 10.3892/ijo.2015.3117].

Abstract 703: Transient G1 cell cycle arrest with trilaciclib enhances the generation of polyfunctional CD4+ and CD8+ T cells

Abstract When administered prior to chemotherapy, intravenous trilaciclib transiently arrests cyclin-dependent kinase 4/6-dependent cells in the G1 phase of the cell cycle, which has been shown to augment antitumor immunity. In an open-label, phase 2 trial in patients with metastatic triple-negative breast cancer (NCT02978716), administering trilaciclib prior to gemcitabine plus carboplatin improved overall survival, potentially through protection and direct activation of immune function. To determine the impact of trilaciclib on effector T-cell function, we conducted in vitro studies to assess the kinetics of cytotoxic function and generation of polyfunctional T cells. Peripheral blood mononuclear cells (PBMCs) or naïve CD4+ and CD8+ T cells were purified from 6 healthy human donors and activated with CD2/3/28 beads. On days 0, 1, and 3 post activation, 100 nM trilaciclib was added to the cells. To visualize phenotypic and functional changes, T cells that had been activated for 3, 7, or 14 days with or without trilaciclib were restimulated with phorbol myristate acetate (PMA) and ionomycin. Dimension-reduction analysis of flow cytometric data was used to identify polyfunctional subpopulations of CD4+ and CD8+ T cells. T-cell clusters with polyfunctional profiles were identified based on the coproduction of IFNγ, TNFα, and IL-2 cytokines. Subsets of polyfunctional clusters were defined according to other immune phenotypic profiles, including T-cell exhaustion (PD-1 and LAG-3), degranulation (granzyme B), proliferation (Ki-67), and transcription factors (TOX, TCF-1, and T-bet). For both CD4+ and CD8+ T cells, 3 polyfunctional subpopulations were identified based on differential expression of granzyme B, PD-1, and TOX. When CD4+ and CD8+ T cells were activated in the presence of trilaciclib and restimulated with PMA and ionomycin, there was an increase in the frequency of polyfunctional T-cell subsets producing granzyme B. This was compensated by a decrease in granzyme Blow polyfunctional cells, which was associated with increased PD-1high and TOXhigh expression. Clustering analysis of trilaciclib-treated PBMCs also revealed a reduction in proliferating CD8+ T cells with undetectable effector cytokine function based on a decrease in the frequency of CD8+ T cells expressing TOXhigh and Ki-67high. Transient G1 cell cycle arrest in T cells may limit T-cell hyperactivation, which has been shown to induce immunopathological responses. The increased generation of polyfunctional CD8+ T cells is associated with enhanced effector T-cell responses and memory T-cell differentiation. In combination with previous data showing increased differentiation of naïve T cells into memory T cells upon exposure to trilaciclib, these data support a beneficial role for trilaciclib in enhancing antitumor T-cell responses. Citation Format: Sarah Ahn, Subing Cao, Melissa A. Russo, John S. Yi. Transient G1 cell cycle arrest with trilaciclib enhances the generation of polyfunctional CD4+ and CD8+ T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 703.

Different degree of cytokinemia and T-cell activation according to serum IL-6 levels in critical COVID-19.

Tocilizumab, a humanized anti-interleukin-6 receptor (IL-6R) antibody, is recommended for the treatment of severe to critical coronavirus diseases 2019 (COVID-19). However, there were conflicting results on the efficacy of tocilizumab. Therefore, we hypothesized that the differences in tocilizumab efficacy may stem from the different immune responses of critical COVID-19 patients. In this study, we described two groups of immunologically distinct COVID-19 patients, based on their IL-6 response. We prospectively enrolled critical COVID-19 patients, requiring oxygen support with a high flow nasal cannula or a mechanical ventilator, and analyzed their serial samples. An enzyme-linked immunosorbent assay and flow cytometry were used to evaluate the cytokine kinetics and cellular immune responses, respectively. A total of nine patients with critical COVID-19 were included. The high (n = 5) and low IL-6 (n = 4) groups were distinguished by their peak serum IL-6 levels, using 400 pg/mL as the cut-off value. Although the difference of flow cytometric data did not reach the level of statistical significance, the levels of pro-inflammatory cytokines and the frequencies of intermediate monocytes (CD14+CD16+), IFN-γ+ CD4+ or CD8+ T cells, and HLA-DR+PD-1+ CD4+ T cells were higher in the high IL-6 group than in the low IL-6 group. There were distinctive two groups of critical COVID-19 according to serum IL-6 levels having different degrees of cytokinemia and T-cell responses. Our results indicate that the use of immune modulators should be more tailored in patients with critical COVID-19.

Comparison of T Lymphocyte Subsets and Natural Killer Lymphocytes in Moderate Versus Severe COVID-19 Patients.

Severe respiratory involvement that follows a process of immune dysregulation and intense cytokine production remains to be the most dreaded complication of Coronavirus Disease-2019 (COVID-19) infection. The aim of this study was to analyze T lymphocyte subsets and natural killer (NK) lymphocytes in moderate and severe cases of COVID-19 infection and assess their significance in disease severity and prognosis. Twenty moderate cases and 20 severe cases of COVID-19 were studied and compared regarding blood picture, biochemical markers, T lymphocyte population subsets, and NK lymphocytes, which were determined by flow cytometric analysis. On analyzing the flow cytometric data of T lymphocyte cells and their subsets and NK cells in two groups of COVID-19 infection (one group moderate and the other severe cases), some immature NK lymphocyte relative and absolute counts were higher in the severe patients with worse outcome and death, while some mature NK lymphocyte relative and absolute counts were depressed in both groups. Also, interleukin (IL)-6 was significantly higher in severe cases when compared to moderate cases, and there was a positive significant correlation between immature NK lymphocyte relative and absolute counts and IL-6. There was no statistically significant difference between T lymphocyte subsets (T helper and T cytotoxic) with disease severity or outcome. Some immature NK lymphocyte subsets contribute to the widespread inflammatory response that complicates severe cases of COVID-19; therapeutic approaches directed to enhancing NK maturation or drugs that block NK cell inhibitory receptors have a potential role in controlling COVID-19 induced cytokine storm.

In-depth immunophenotyping reveals significant alteration of lymphocytes in buffalo with brucellosis.

Water buffalo (Bubalus bubalis) has a prominent position in the livestock industry worldwide but still suffers from limited knowledge on the mechanisms regulating the immune against infections, including brucellosis (BRC), one of the most significant neglected zoonotic diseases of livestock. Seventy-three buffalo were recruited for the study. Thirty-five were naturally infected with Brucella spp. The aims of the study were to (i) verify the cross-reactivity of 16 monoclonal antibodies (mAbs) developed against human, bovine, and ovine antigens; (ii) evaluate lymphocyte subset alterations in BRC positive buffalo; (iii) evaluate the use of the canonical discriminant analysis (CDA), with flow cytometric data, to discriminate BRC positive from negative animals. A new set of eight mAbs (anti CD3e, CD16, CD18, CD45R0, CD79a; CD172a) were shown to cross-react with water buffalo orthologous molecules. BRC positive animals presented a significant (p < 0.0001) decrease in the percentage of PBMC (29.5 vs. 40.3), total, T and B lymphocytes (23.0 vs. 35.5, 19.2 vs. 28.9, 2.6 vs. 5.7, respectively). In contrast, they showed an increase in percentage of granulocytes (65.2 vs. 55.1; p < 0.0001) and B lymphocytes CD21neg (22.9 vs. 16.1; p=0.0067), a higher T/B lymphocyte ratio (10.3 vs. 6.4; p=0.0011) and CD3+ /CD21+ (14.7 vs. 8.3; p=0.0005) ratio. The CDA, applied to 33 different flow cytometric traits, allowed the discrimination of all BRC positive from negative buffalo. Although this is a preliminary study, our results show that flow cytometry can be used in a wide range of applications in livestock diseases, including in support of uncertain BRC diagnoses.

Retracted] MicroRNA‑122 affects cell aggressiveness and apoptosis by targeting PKM2 in human hepatocellular carcinoma.

Following the publication of both the above article and a corrigendum (doi:10.3892/or.2021.8073) that was concerned with the correction of overlapping data panels in Figs.6 and 7, it has been drawn to the Editors' attention by a concerned reader that the proposed replacement cell invasion assay shown in the revised version of Fig.7A, and also flow cytometric data featured in Fig.5A and C, were strikingly similar to data appearing in different form in other articles by different authors. Owing to the fact that these contentious data in the above article had already been published elsewhere, or were already under consideration for publication, prior to its submission to Oncology Reports, the Editor has decided that this paper should be retracted from the Journal. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor apologizes to the readership for any inconvenience caused. [Oncology Reports 34: 2054‑2064, 2015; DOI: 10.3892/or.2015.4175].

Technical versus biological variability in a synthetic human gut community

ABSTRACT Synthetic communities grown in well-controlled conditions are an important tool to decipher the mechanisms driving community dynamics. However, replicate time series of synthetic human gut communities in chemostats are rare, and it is thus still an open question to what extent stochasticity impacts gut community dynamics. Here, we address this question with a synthetic human gut bacterial community using an automated fermentation system that allows for a larger number of biological replicates. We collected six biological replicates for a community initially consisting of five common gut bacterial species that fill different metabolic niches. After an initial 12 hours in batch mode, we switched to chemostat mode and observed the community to stabilize after 2–3 days. Community profiling with 16S rRNA resulted in high variability across replicate vessels and high technical variability, while the variability across replicates was significantly lower for flow cytometric data. Both techniques agree on the decrease in the abundance of Bacteroides thetaiotaomicron, accompanied by an initial increase in Blautia hydrogenotrophica. These changes occurred together with reproducible metabolic shifts, namely a fast depletion of glucose and trehalose concentration in batch followed by a decrease in formic acid and pyruvic acid concentrations within the first 12 hours after the switch to chemostat mode. In conclusion, the observed variability in the synthetic bacterial human gut community, as assessed with 16S rRNA gene sequencing, is largely due to technical variability. The low variability seen in HPLC and flow cytometry data suggests a highly deterministic system.

Quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes

Biotic interactions are known as a major control on microbial diversity. However, biotic interactions have rarely been quantified in an adequate manner, often leaving much residual variation unexplained in microbial biogeographic studies. Herein, we propose a holistic approach to disentangle the relative importance of inter-domain interactions in shaping microbial diversity by incorporating community-level characteristics. Taking coastal bacterioplankton on a regional scale as an example, we designated a range of community characteristics of pico- and nanoeukaryotes derived from metabarcoding and flow cytometric data as inter-domain interacting proxies, which were then considered in statistical modeling. We found that the bacterial diversity indices and community structure were much more accurately explained by a number of eukaryotic characteristics than by the measured environmental variables and/or spatial variables alone, as were the richness, relative abundances, and assemblage structures of major bacterial taxa. In co-occurrence networks, the nodes of characteristics that had more edges (links) were frequently the best explanatory variables for bacterial diversity indices. Over 70% of total variation in bacterial community structure could be explained by three categories of biotic interactions: parasitism (27%), fungi-bacterial competition (32%), and trophic structure and bacterivory (13%). This study showcases a methodological framework to infer different types of inter-domain interactions at play, and stresses the importance of non-grazing interacting processes in shaping bacterial diversity and community assembly.

UROLITHIN A: GUT-BRAIN DIETARY INTERVENTION IN PARKINSON'S DISEASE

Abstract Gastrointestinal dysfunction is amongst the most common prodromal symptoms of Parkinson’s disease (PD). Pathological alpha-synuclein has been detected in the intestines prior to disease onset, and a leaky gut is also implicated in its etiology. Thus, we hypothesized that modulation of the gut microbiome and intestinal immune milieu via early dietary intervention may act to mitigate PD pathogenesis. Urolithin A (UA) is a gut metabolite shown to ameliorate geriatric diseases by increasing mitophagy and dampening inflammation. The aim of our study is to elucidate its mechanism of action and therapeutic efficacy in PD, which to date is unclear. Preliminary flow cytometric data demonstrates that administration of a UA-diet significantly increased the proportion of colonic gamma-delta (γδ) T cells in nine-month-old Thy-1 α-syn mice, which are downregulated relative to non-transgenics on a non-UA control diet. PD patients have been reported to have higher levels of γδ T cells in their cerebrospinal fluid and, while little is known about colonic γδ Ts in the context of PD, these cells are anti-inflammatory and responsible for intestinal repair in several colitis models. Our data suggests a retention of lymphocytes involved in the targeted migration from the gut to the brain, which may contribute to gut epithelial integrity. Proportion of induced regulatory T cells in peripheral blood, which are critical for immune tolerance, also increased significantly with a UA-diet. In addition, UA-fed mice showed a slight improvement in novel object recognition. Additional analyses are underway to comprehensively evaluate the impact of UA on PD pathology.

Retracted] Overexpression of miR‑203 increases the sensitivity of NSCLC A549/H460 cell lines to cisplatin by targeting Dickkopf‑1.

Following the publication of this paper, it was drawn to the Editors' attention by a concerned reader that certain of the mouse images shown in Fig.5A and the flow cytometric assay data shown in Fig.4A and B were strikingly similar to data appearing in different form in other articles written by some of the same authors, but which had already been published elsewhere or were already under consideration for publication, prior to this paper's submission to Oncology Reports. In view of the fact that these apparent duplications of data have come to light, the Editor of Oncology Reports has decided that this paper should be retracted from the Journal. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a satisfactory reply. The Editor apologizes to the readership for any inconvenience caused. [Oncology Reports 37: 2129‑2136, 2017; 10.3892/or.2017.5505].

Commercial DURAClone panels for extending the repertoire of multicolour immunophenotypic panels in an academic flow cytometry laboratory in South Africa.

Commercial multicolour fixed immunophenotyping panels can improve flow cytometric diagnostic immunophenotyping repertoire. This study validated the commercially available, standardised Beckman Coulter lyophilised DURAClone RE panels to discriminate specific haematolymphoid subtypes. We compared the diagnostic capability of the DURAClone acute leukaemia B (ALB), chronic leukaemia B (CLB), and plasma cells (PC) panels to the predicate second-line panels in Charlotte Maxeke Johannesburg Academic Hospital, Johannesburg, South Africa, from April to August 2020. Clinical diagnostic concordance between the in-house second-line immunophenotyping (the predicate method) and DURAClone was established. The ALB panels tested for precursor B-cell acute lymphoblastic leukaemia (n = 11) or normal bone marrow haematogones (n = 9); CLB panels established haematolymphoid subtypes of mature B-cell lymphoproliferative disorders (B-LPD) (n = 20), while PC panels detected plasma cell dyscrasias (PCD) (n = 17). Flow cytometer setup and data interpretation to discriminate normal and aberrant immunophenotypes were per manufacturer's instructions. There was 100% clinical diagnostic concordance between the predicate and the test panels for second-line diagnostic investigation of B-ALL (with additional CD56), mature B-LPD (with additional discernment of CD81, ROR-1, CD79b and CD43) and PCD. The DURAClone CLB exceeded the predicate second-line performance, offering extended second-line diagnostic discernment of mature B-LPD subtypes and discernment of CD5+ B-LPD from other non-CD5+ (or CD5-) B-LPD; likewise, the PC panels enabled discovery of PCD. While ALB testing offered no additional diagnostic advantage over existing predicate investigation, CD58 did offer additional information to discern haematogones from B-ALL.

Retracted] Exosome‑mediated transfer of lncRNA RP11‑838N2.4 promotes erlotinib resistance in non‑small cell lung cancer.

Following the publication of this paper, it was drawn to the Editors' attention by a concerned reader that various panels showing the cellular data in Fig.1B, the western blotting data shown in Figs.2B and 3C, the TEM scanning data shown in Fig.4B, the ChIP assay data in Fig.2F and the flow cytometric assay data in Fig.3D were strikingly similar to data appearing in different form in other articles by different authors from different research institutions. Owing to the fact that the contentious data in the above article had already been published, or were already under consideration for publication elsewhere, prior to its submission to International Journal of Oncology, the Editor has decided that this paper should be retracted from the Journal. The authors independently contacted the Editorial Office to request the retraction of this paper. The Editor apologizes to the readership for any inconvenience caused. [International Journal of Oncology 53: 527‑538, 2018; DOI: 10.3892/ijo.2018.4412].

Interoperable vocabulary for marine microbial flow cytometry

The recent development of biological sensors has extended marine plankton studies from conducting laboratory bench work to in vivo and real-time observations. Flow cytometry (FCM) has shed new light on marine microorganisms since the 1980s through its single-cell approach and robust detection of the smallest cells. FCM records valuable optical properties of light scattering and fluorescence from cells passing in a single file in front of a narrow-collimated light source, recording tens of thousands of cells within a few minutes. Depending on the instrument settings, the sampling strategy, and the automation level, it resolves the spatial and temporal distribution of microbial marine prokaryotes and eukaryotes. Cells are usually classified and grouped on cytograms by experts and are still lacking standards, reducing data sharing capacities. Therefore, the need to make FCM data sets FAIR (Findability, Accessibility, Interoperability, and Reusability of digital assets) is becoming critical. In this paper, we present a consensus vocabulary for the 13 most common marine microbial groups observed with FCM using blue and red-light excitation. The authors designed a common layout on two-dimensional log-transformed cytograms reinforced by a decision tree that facilitates the characterization of groups. The proposed vocabulary aims at standardising data analysis and definitions, to promote harmonisation and comparison of data between users and instruments. This represents a much-needed step towards FAIRification of flow cytometric data collected in various marine environments.

Integrated machine learning approaches for flow cytometric quantification of myeloid-derived suppressor cells in acute sepsis.

Highly heterogeneous cell populations require multiple flow cytometric markers for appropriate phenotypic characterization. This exponentially increases the complexity of 2D scatter plot analyses and exacerbates human errors due to variations in manual gating of flow data. We describe a semi-automated workflow, based entirely on the Flowjo Graphical User Interface (GUI), that involves the stepwise integration of several, newly available machine learning tools for the analysis of myeloid-derived suppressor cells (MDSCs) in septic and non-septic critical illness. Supervised clustering of flow cytometric data showed correlation with, but significantly different numbers of, MDSCs as compared with the cell numbers obtained by manual gating. Neither quantification method predicted 30-day clinical outcomes in a cohort of 16 critically ill and septic patients and 5 critically ill and non-septic patients. Machine learning identified a significant decrease in the proportion of PMN-MDSC in critically ill and septic patients as compared with healthy controls. There was no difference between the proportion of these MDSCs in septic and non-septic critical illness.

Single-Cell Multiomics of Pre- and Post- Remission Bone Marrow from a Pediatric Patient with JMML Reveal Distinct Progenitors and Inflammatory Monocytes Which Are Eliminated By Chemotherapy

Juvenile myelomonocytic leukemia (JMML) is a rare myeloproliferative/myelodysplastic disorder with a poor prognosis. Unlike many other hematologic malignancies, it remains challenging to rapidly diagnose, and levels of residual disease are challenging to measure. Ongoing efforts to characterize the pathophysiology of JMML have yielded significant insight, but a complete picture of the cellular hierarchy of the disease has been elusive. To obtain an unsupervised view of the heterogeneity of hematopoietic cell types in JMML, we performed single-cell RNA multiomics on serial samples from a 4-year-old patient who presented with fevers, hepatosplenomegaly, leukocytosis, monocytosis, elevated Hemoglobin F, and bone marrow blast count of 7%. Clinical sequencing revealed mutations in PTPN11 (VAF 66%), NF1 (VAF 17%), MYB (VAF 12%), and SH2B3. We obtained a sample for multiomic profiling following his initial cycles of chemotherapy when PTPN11 variant frequency was 32%, and he remained critically ill. The subsequent sample (6 weeks later) was collected when no mutated PTPN11 was detectible, and his clinical picture had significantly improved. Both samples were processed using gradient centrifugation, and a subset of the cells captured was immunomagnetically enriched CD34+ cells. A panel of 141 oligonucleotide-tagged antibodies was used to label the cells prior to single-cell capture. The resulting libraries were sequenced using standard short-read sequencing, and reads were demultiplexed and counted using common workflows. Single-cell profiles were visualized using UMAP. Cells were annotated using a highly performant machine-learning algorithm that classifies cells using a healthy reference of human BM, which we have previously validated using publicly available data. Examining all cells together, we observed changes in two key cell types over time (Figure 1). First, we noted a large population of monocyte-like cells with increased expression of genes associated with complement, inflammation (including IL1beta), and KRAS signaling in the pre-remission sample, which nearly completely disappeared in the second sample. Moreover, surface expression of CD39 was significantly higher in these cells than in all other cells measured (Figure 2). These data suggest CD39 may represent a novel biomarker to monitor treatment response in JMML or as a potential target for therapy. Second, we observed two distinct hematopoietic stem cell (HSC) populations in the pre-remission sample and only one in the remission sample. Differential gene expression analysis between the two pre-remission HSC clusters revealed enrichment of specific genes previously associated with JMML progenitors in one, including HOPX. This population of HSCs also exhibited higher expression CD7, which correlated with the patient's flow cytometric data from the same time point. We hypothesize that CD7/HOPX expressing HSCs in the first sample were JMML progenitor cells eliminated by therapy. Our work demonstrates the power of single-cell multiomics to dissect cellular heterogeneity in diseases with complex clonal hierarchies. Moreover, these data can also be a resource for identifying potential biomarkers and drug targets. Specifically, our data suggest that inflammatory monocytes may be a distinct target for therapy, which may benefit critically ill patients like ours. Future efforts will be focused on profiling additional patients and rigorously characterizing the mutational state of individual cells. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Identification of Minimal Residual Disease in B-Acute Lymphoblastic Leukaemia Using Uniform Manifold Approximation Projection

Background and objective: Acute Lymphoblastic Leukaemia (ALL) is the most common childhood cancer, with B lineage ALL accounting for approximately 85% of cases. Minimal residual disease (MRD) levels during induction therapy are important prognostic biomarkers that allow patient stratification dependent on their risk of relapse. MRD is quantified by molecular analyses of antigen receptor gene rearrangements or by flow cytometric evaluation of aberrant immunophenotypes. The latter method is increasingly being used worldwide, due to its reduced cost, its provision of predictive biomarkers for antigen therapies and the inclusion of both MRD methodologies in contemporary clinical trials. However, accurate detection of MRD by flow cytometry is complex, time-consuming and relies heavily on user training and expertise. Machine learning has been evolving in the field, with the aim of superseding manual analysis of Flow MRD data. This study aimed to test the ability of the unsupervised machine learning dimensional reduction algorithm, Uniform Manifold Approximation Projection, (UMAP) to visualise and accurately quantify 'MRD islands' from normal cell populations with minimal human intervention. Methods: Flow cytometric data from bone marrow samples from children entered on the UKALL2011 clinical trial were used in the study. For B lineage ALL, the protocol included the identification of a leukaemia associated immunophenotype at diagnosis using 2 antibody combinations and 'on treatment' samples were then ran with the most appropriate antibody tube. In this study, we used data from Euro tube 1 which included the antibodies, CD20 FITC, CD38 PE, CD34 PerCP, CD10 PE-Cy7, CD19 APC, CD45 APC-Cy7 and the nuclear dye, Syto 41, tagged with Pacific Blue. FCS files were uploaded to Cytobank and pre-gated for CD19 positive cells prior to running the UMAP algorithm. Each data set was ran alongside data from 10 normal marrows stained with Euro tube 1. Results: Initially, a selection of samples with varying MRD levels (n=6) were ran using UMAP with a range of minimum distance and number of neighbours parameters. A minimum distance of 0.1 and number of neighbours of 13 was the optimal UMAP settings to produce a very clear separation of MRD islands from healthy cells populations across all samples. (Figure 1). These setting were then used for 341 B-ALL MRD samples at different time points day 8 (n=116), day 15 (n=32), days 29 (n=118), weeks 6-20 (n= 75). MRD levels from UMAP were compared to those generated by the gold standard sequential gating. Excellent concordance was seen, as demonstrated by an R2 value of 0.99 (linear correlation). For 'on treatment samples', UMAP showed excellent accuracy with sensitivity and specificity values of 93% and 100%, respectively. Positive predictive values were 100% while negative predictive values were 94% due to UMAP analysis failing to identify an MRD island in 11 'on treatment' samples, 9 at day 8 and 2 at day 28. The average UMAP run time was less than 2 minutes. Conclusion: Detection of MRD using the UMAP algorithm showed an excellent correlation with the gold standard sequential gating technique. False negatives were due to overlap of antigen expression with normal cells and/or low ALL cell numbers. Current Flow MRD methodologies use 8 or more antibodies and higher target cell numbers, which may reduce the false negative UMAP call rate. In summary, UMAP has promising ability to simplify the downstream data analyses of Flow MRD, overcoming the extensive training needed, human subjectivity and will be time and thus cost saving. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal