Cytometry Part B: Clinical Cytometry最新文献

Flow cytometry is a powerful tool for analyzing diverse cellular properties, making it essential in immunology research, clinical trials, and diagnostics. Integrating artificial intelligence (AI) into flow cytometry has the potential to enhance various aspects of assay development and application, including reagent selection, instrument standardization, panel and assay design, data analysis, quality controls, and knowledge dissemination. This paper provides a review of current AI applications in flow cytometry and explores the potential future directions for AI integration in the field.

CD300e is a marker of mature monocytes in flow cytometry; however, there is limited detailed information on staining patterns in conjunction with other monocyte markers. We evaluated the flow cytometric staining patterns of CD64, CD14, and CD300e in 12 negative and 33 positive peripheral blood specimens and 16 negative and 56 positive bone marrow specimens. The positive cases were involved by myeloid neoplasms (increased blasts and/or abnormal monocytes). Flow cytometry plots were reviewed for each case, the monocyte population was identified by bright CD64 expression, and the monocyte maturation pattern was visualized by CD14 versus CD300e plots. Peripheral blood and bone marrow differential counts were collected. A total of 39% (22/56) of the positive bone marrow cases showed a different maturation pattern from the negative bone marrow cases. Of the positive peripheral blood cases, 28/33 (85%) showed a CD14 by CD300e pattern different from that observed in the negative peripheral bloods. When the subset of bone marrow cases involved by monocytic neoplasms was evaluated, there was no significant difference between monocyte percentage by flow cytometry versus morphology and between blast plus promonocyte percentage by flow cytometry versus morphology. We conclude that isolation of monocytes by bright CD64 expression and low side-scatter and subsequent evaluation of the CD14/CD300e maturation pattern may help identify myeloid neoplasms. Quantification of CD64 + CD14- and/or CD64 + CD300e- cells by flow cytometry may aid blast/blast equivalent identification/quantification.

Immunophenotyping by flow cytometry is a valuable test providing important information in a timely manner. In clinical laboratories, it is performed using validated antibody panels designed to ensure consistent and accurate results. However, unforeseen situations, such as unique or unusual immunophenotypes, or supply chain issues, may necessitate ad hoc modifications to these panels. This manuscript provides guidance for performing minor modifications, such as substituting or adding one or two antibodies, while maintaining the integrity of the assay. These modifications are intended for rare clinical situations and are not substitutes for the full validation protocols outlined in CLSI H62. An example of this would be a patient with a rare, but not uncommon, situation in which a B cell lymphoma lacks expression of CD19, CD20, and surface light chains, such that the lineage of the neoplastic cells cannot be determined without a straightforward addition or substitution of another marker into a laboratory's available panel. The recommendations and best practices herein aim to optimize patient care by allowing laboratories to adapt to unique clinical scenarios without compromising assay performance and are not a way to permanently modify the assay. Key considerations include assessing the impact on fluorescence compensation, antibody binding, assay sensitivity, and overall assay performance. The manuscript provides limitations for the extent of modifications, examples, and troubleshooting strategies to ensure reliable results when ad hoc changes are made. Proper documentation with review and approval by laboratory medical directors is recommended to mitigate risks associated with these modifications.

Acute promyelocytic leukemia (APL) is a medical emergency that needs immediate diagnosis and treatment. Podoplanin, a transmembrane glycoprotein that binds CLEC-2 on platelets, was recently demonstrated to be abnormally expressed in leukemic blasts in APL, as opposed to other forms of AML, in a study using thawed primary cells. This study aimed to explore and validate the diagnostic accuracy of measuring podoplanin expression by flow cytometry in the differential diagnosis of APL and other forms of acute myeloid leukemia (AML) as part of the diagnostic work-up of all cases suspected of AML in an academic hematology center. Podoplanin expression was measured by flow cytometry in bone marrow samples obtained at disease presentation from all consecutive adult patients suspected of AML. Results from 24 APL patients were compared with those from 23 non-APL AML patients matched by age and sex. Markedly higher PDPN expression was observed in APL patients when compared to other AML patients, with an area under the curve of 0.92 (95%CI: 0.82-1.0, p < 0.0001) for the percentage of positive cells. Combining an optimal cutoff of 7.66% for PDPN-positive blasts and 1691 for the mean fluorescence index of PDPN expression, APL was identified with a sensitivity of 87.5% and a specificity of 100%. Moreover, PDPN expression presented a negative correlation with platelet count and fibrinogen levels. PDPN expression measured by flow cytometry can accurately differentiate between APL and other forms of AML in a real-world clinical setting, contributing to the diagnosis of this form of acute leukemia. If confirmed in larger prospective studies, the negative association of PDPN expression with fibrinogen and platelet counts supports the concept that this biomarker can potentially contribute to the clinical characterization of APL.

Two types of plasmacytoid dendritic cell (pDC) proliferation disease are acknowledged so far by the 5th edition of the World Health Organization Classification of Haematolymphoid Tumors: Blastic plasmacytoid dendritic cell neoplasm (BPDCN) and mature pDC proliferation associated with myeloid neoplasms (MPDCP) in which pDC is part of the malignant clone. We aim to investigate pDC proliferation associated with non-myeloid acute leukemia (AL). A retrospective analysis of all cases admitted in our center with a diagnosis of non-myeloid AL from September 2020 to April 2023 was performed to select cases with pDCs greater than 2% of bone marrow by flow cytometry (FCM). We conducted comprehensive analyses encompassing FCM immunophenotyping, histopathological examination, morphological assessment, cytogenetic studies, and molecular genetic profiling across all cases. Proliferation of pDCs was detected in 10 (0.88%) out of 1140 non-myeloid AL patients by FCM, including 4/944(0.42%) cases of B lymphoblastic leukemia (B-ALL), 3/95 (3.16%) cases of T lymphoblastic leukemia (T-ALL) and 3/101 (2.97%) cases of acute leukemia of ambiguous lineage (ALAL) (p = 0.009). The 4 cases of B-ALL were all Philadelphia Chromosome positive (Ph+). PDCs in 3 out of 10 patients expressed CD56 (37.5%), 8/10 expressed CD7 (80%), 9/10 expressed CD303 (90%), all expressed CD304 (100%), and 5 of 8 evaluable cases were positive for CD34 (62.5%). In cases in which pDCs expressed CD7 and/or CD56, the blast cells all expressed CD7 and/or CD56 as well; the pDCs in all B-ALL patients expressed CD19. FCM dot plot in 2 of the B-ALL-pDC showed a spectrum from blast cells to pDCs: CD303 and CD304 gradually emerged as CD34 disappeared. Among the 8 patients who underwent bone marrow biopsy, pDCs exhibited two distinct distribution patterns: pure interstitial infiltration in 6 cases (75%) and focal/scattered clusters against an interstitial background in 2 cases (25%). NRAS showed recurrent mutations at identical genomic positions. Each NRAS variant (c.35G>A and c.38G>T) was detected twice across three patients. FCM could effectively detect pDC proliferation in non-myeloid leukemia, which occurred at a significantly higher incidence in T-ALL and ALAL than in B-ALL. In B-ALL, it was associated with the Ph + subtype. PDCs and blast cells shared some lymphoid antigens that were uncommon in AML-pDC or BPDCN. In the bone marrow, pDCs were predominantly characterized by an interstitial infiltration pattern.

CD38 and CD138 are important diagnostic markers in flow cytometric analysis of plasma cells (PC) in the context of multiple myeloma (MM). Anti-CD38 therapy, such as daratumumab, exacerbates CD38 detection. In addition, CD138 can be degraded and is then no longer easily detectable on the cell surface. Variable heavy domain heavy chain antibodies (VHH) are single variable domain antibody fragments. Clone JK36 consists of two anti-CD38 VHH fragments and allows targeting of a cryptic CD38 epitope that is not accessible to conventional antibodies (CA). Therefore, our aim was to test VHH in comparison to our conventional anti-CD38 antibody (LS198) in MM bone marrow samples after daratumumab therapy (d-t) compared to therapy-naïve (n) and samples with unknown therapy. A total of 111 samples were analyzed (n = 11 n, n = 81 d-t, n = 18 with unknown therapy). While CD38 was equally well detected by VHH and CA in therapy-naïve samples, CD38 could only be detected in 8% of d-t samples with CA but in 91% with VHH. This resulted in an overall significant reduction in the number of detectable PC, and three samples with undetectable PC by CA compared to VHH. Furthermore, CD138 was reduced/degraded in 52% of d-t samples of which 88% had undetectable CD38 by CA. In addition to proper detection of CD38, VHH is also able to determine a potential CD38 reduction of cell surface expression, as shown by a reduction in CD38 median fluorescence intensity (MFI) on d-t compared to n samples. One d-t sample revealed two distinct PC populations differing by dim and bright CD38 expression, only detectable by VHH. Interestingly, samples with unknown treatment history can be grouped into scenarios most likely treated with daratumumab, or rather treatment-naïve, respectively. In summary, VHH provides superior CD38 detection in d-t MM patients, which is vital for diagnostic samples, and it is capable of providing information about CD38 integrity on the cell surface.

Flow cytometric analysis plays an important role in the diagnosis of chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL). Most CLL cases show a typical immunophenotype characterized by the expression of CD5, CD23, CD43, ROR1, and CD200, along with dim expression of B-cell markers. However, some show an atypical immunophenotype. The immunophenotypic, cytogenetic, and mutational profiles of atypical CLL are not well characterized. In this study, we aim to address these gaps by analyzing a cohort of 270 CLL cases with a focus on those with atypical immunophenotypes. Their detailed immunophenotype as assessed by flow cytometry is presented along with cytogenetics and mutational data. Fifty-three (19.6%) cases exhibited an atypical immunophenotype. The common atypical immunophenotypic features detected included increased CD20 in 17 (32.1%), negative CD43 in 17 (32.1%), negative ROR1 in 16 (30.1%), and increased surface light chain in 11 (20.8%) cases. Trisomy 12 was more frequently detected in atypical versus typical CLL cases (58.5% vs. 20.7%, p < 0.01), including those with decreased to negative expression of CD5, CD23, CD43, and ROR1, and increased expression of CD20 and CD22. Cases with increased CD20 expression more often had mutated IGHV. BIRC3 is the most frequent mutation in the atypical CLL group, and alterations in BIRC3 (p = 0.02), KRAS (p = 0.03), NRAS (p < 0.01), KMT2D (p < 0.01), and SMARCA4 (p = 0.02) were more frequently detected in atypical CLL when compared to typical CLL. In summary, approximately 20% of CLL cases show an atypical immunophenotype, and these cases have cytogenetic abnormalities and mutation profiles that differ in frequency from typical CLL cases. Recognition of the immunophenotype of atypical CLL can be helpful in the diagnosis and differential diagnosis in low-grade B-cell neoplasms.