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. 2024 Jul 12:15:1402038.
doi: 10.3389/fimmu.2024.1402038. eCollection 2024.

Flow cytometry-based diagnostic approach for inborn errors of immunity: experience from Algeria

Azzeddine Tahiat  1 , Reda Belbouab  2 , Abdelghani Yagoubi  3 , Saliha Hakem  2 , Faiza Fernini  2 , Malika Keddari  2 , Hayet Belhadj  4 , Souad Touri  5 , Samira Aggoune  6 , Jennifer Stoddard  7 , Julie Niemela  7 , Farida Zerifi  8 , Souhila Melzi  9 , Rawda Aboura  9 , Amina Saad-Djaballah  10 , Yacine Ferhani  2 , Abdalbasset Ketfi  11 , Hassen Messaoudi  12 , Tahar Bencharif Madani  13 , Zouleikha Benhacine  14 , Abdelhak Dehimi  15 , Kamelia Okka  15 , Fairouz Amroune  15 , Meriem Fellahi  15 , Chafa Bendahmane  16 , Radia Khoulani  16 , Asma Oukil  1 , Asma Soufane  1 , Imene Bourelaf  1 , Chahynez Boubidi  17 , Nadia Boukhenfouf  18 , Mohamed Amine Ifri  19 , Noureddine Khelafi  19 , Houda Boudiaf  20 , Tahar Khelifi Touhami  21 , Fethi Meçabih  22 , Malika Boucelma  23 , Amara Zelaci  24 , Ourida Gacem  25 , Mohamed Samir Ladj  25 , Azzedine Mekki  26 , Nadia Bensaadi  27 , Malika Benhalima  28 , Zoulikha Zeroual  17 , Belkacem Bioud  15 , Mustapha Benameur  12 , Rachid Bouhdjila  14 , Zahir Bouzerar  9 , Ouardia Ibsaine  8 , Hachemi Maouche  6 , Leila Kedji  5 , Leila Smati  10 , Rachida Boukari  2 , Claude Lambert  29 , Sergio D Rosenzweig  7 , Luigi D Notarangelo  30 , Kamel Djenouhat  1
Affiliations

Flow cytometry-based diagnostic approach for inborn errors of immunity: experience from Algeria

Azzeddine Tahiat et al. Front Immunol. .

Abstract

Purpose: In this study, we retrospectively reviewed the use of flow cytometry (FCM) in the diagnosis of inborn errors of immunity (IEIs) at a single center in Algeria. Sharing insights into our practical experience, we present FCM based diagnostic approaches adapted to different clinical scenarios.

Methods: Between May 2017 and February 2024, pediatric and adult patients presenting with clinical features suggestive of immunodeficiency were subjected to FCM evaluation, including lymphocyte subset analysis, detection of specific surface or intracellular proteins, and functional analysis of immune cells.

Results: Over a nearly seven-year period, our laboratory diagnosed a total of 670 patients (372 (55.5%) males and 298 (44.5%) females), distributed into 70 different IEIs belonging to 9 different categories of the International Union of Immunological Societies classification. FCM was used to diagnose and categorize IEI in 514 patients (76.7%). It provided direct diagnostic insights for IEIs such as severe combined immunodeficiency, Omenn syndrome, MHC class II deficiency, familial hemophagocytic lymphohistiocytosis, and CD55 deficiency. For certain IEIs, including hyper-IgE syndrome, STAT1-gain of function, autoimmune lymphoproliferative syndrome, and activated PI3K delta syndrome, FCM offered suggestive evidence, necessitating subsequent genetic testing for confirmation. Protein expression and functional assays played a crucial role in establishing definitive diagnoses for various disorders. To setup such diagnostic assays at high and reproducible quality, high level of expertise is required; in house reference values need to be determined and the parallel testing of healthy controls is highly recommended.

Conclusion: Flow cytometry has emerged as a highly valuable and cost-effective tool for diagnosing and studying most IEIs, particularly in low-income countries where access to genetic testing can be limited. FCM analysis could provide direct diagnostic insights for most common IEIs, offer clues to the underlying genetic defects, and/or aid in narrowing the list of putative genes to be analyzed.

Keywords: complement deficiencies; flow cytometry; flow cytometry-based diagnostic approach; functional assays; lymphocyte phenotyping; protein expression assays.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Flow cytometry-based diagnosis of IEI at our center. (A) Flow cytometry has proven instrumental in diagnosing and categorizing IEI in 514 patients (77%). (B) Among the 514 patients (internal circle), flow cytometry analysis led to the definitive diagnosis in 104 cases (external circle). IEI, inborn errors of immunity.
Figure 2
Figure 2
T-cell subpopulation analysis in a patient with hypomorphic ADA deficiency and an age-matched healthy control. T cells from the patient, carrying a homozygous ADA variant (c.965T>C= p.Phe322Ser), exhibit a memory phenotype (CD45RO+) with nearly absent native (CD45RA+CCR7+) subsets. ADA, adenosine deaminase.
Figure 3
Figure 3
(A) Dot plots and histograms showing reduced IL-17A production (upper left panel), CD45RO+CCR6+CXCR3– Th17 cells (upper middle panel), and pSTAT3 levels after IL-6 stimulation (upper right panel) in a patient with HIES. (B) Degranulation assay of resting NK cells showing impaired CD107a surface expression in a patient with FHL3. (C) Histograms showing partial perforin expression defect on gated CD3–CD56+ NK cells in a patient with FHL2. FHL, familial hemophagocytic lymphohistiocytosis; HIES, hyper-IgE syndrome; IC, isotypic control; pSTAT3, phosphorylated signal transducer and activator of transcription 3.
Figure 4
Figure 4
B-cell subpopulation analysis in two CVID patients and one healthy control. CVID patient (A) has reduced memory B-cell subsets including switched memory (CD27+sIgD-) and non-switched memory (CD27+sIgD+) B cells (middle panel). CVID patient (B) shows an expansion of CD21lo B cells (right panel). CVID, common variable immunodeficiency; sm, switched memory; nsm, non-switched memory.
Figure 5
Figure 5
Dot plots showing complete loss of CD46 (A) and CD55 (B) expression in two patients with aHUS and CHAPLE disease, respectively. Carrier parents from both families show monomodal and intermediate expression of CD46 and CD55. aHUS, atypical hemolytic uremic syndrome; CHAPLE, complement hyperactivation angiopathic thrombosis and protein-losing enteropathy.
Figure 6
Figure 6
Flow cytometric workup based on the clinical presentation. (A), FCM based strategy in the context of nonspecific IEI manifestations. (B), diagnostic strategy in the context of specific clinical presentations. T1 panel, CD3, CD4, CD8, CD45RA, CD45RO, CCR7; T3 panel, CD3, CD4, CD45RO, CXCR3, CCR6, CXCR5; T4 panel, TCRαβ, TCRγδ, CD3, CD4, CD8, CD45; B panel, CD19, CD27, sIgD, CD38, CD24, CD21. ALPS, autoimmune lymphoproliferative syndrome; AR, autosomal recessive; CGD, chronic granulomatous disease; CHAPLE, complement hyperactivation angiopathic thrombosis and protein-losing enteropathy; CHS, Chediak-Higashi syndrome; CID, combined immunodeficiency; CVID, common variable immunodeficiency; DHR, Dihydrorhodamine; GS2, Griscelli syndrome type 2; HIES, hyper-IgE syndrome; HIMS, hyper-IgM syndrome; LAD, leukocyte adhesion deficiency; LRBA, LPS-responsive beige-like anchor protein; SCID, severe combined immunodeficiency; STAT, signal transducer and activator of transcription; XLA, X-linked agammaglobulinemia.
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