Human inborn errors of immunity (IEIs) are a heterogenous group of monogenetic disorders characterized by an absent or aberrant function in one or more components of the immune system, which predisposes affected individuals to increased frequency and severity of infection, autoimmunity, allergy, and malignancy (1, 2). IEIs were traditionally viewed as rare disorders. However, recent reports suggest that IEIs are more common than previously believed, with a collective prevalence ranging from 1/5000 to 1/1000 (3). Furthermore, the wider usage of next-generation sequencing (NGS) platforms had greatly contributed to the discovery of new IEIs and broadened the spectrum of clinical phenotypes associated with IEI (4, 5).

The latest report from the International Union of Immunological Societies (IUIS) Expert Committee described 485 different disorders, categorized into ten groups (1). Most disorders in the IUIS classification have an autosomal recessive (AR) mode of inheritance, suggesting IEIs should be even more prevalent in consanguineous populations from North African countries like Algeria. However, the reported prevalence of ~1/50,000 in the Algerian registry remains low (6, 7). This means there is a significant underestimation of the burden of IEIs in Algeria. Several factors are likely culprits: insufficient awareness, a high frequency of severe forms–especially combined immunodeficiencies–linked to early mortality, and limitations in the availability of necessary diagnostic tools (6, 8).

The basic clinical and laboratory evaluation of a patient with a suspected IEI should encompass a detailed clinical history, a complete blood count (CBC), measurements of serum immunoglobulin (Ig) levels, and complement protein assays (9). Due to the heterogeneity of the clinical presentations of patients, diagnosing IEIs based solely on clinical and conventional laboratory findings can often be challenging. While more recently available genetic investigations serve as definitive tool for diagnosing IEIs, genetic tests are time-consuming, labor-intensive, and costly. Additionally, targeted single-gene tests may overlook causative mutations. Exome sequencing has recently garnered attention as a gold standard for diagnosing IEIs. However, exome sequencing still identifies causative genetic defects in only 40% to 58% of patients (10, 11). Moreover, establishing such a facility in the context of a developing country is highly challenging. Therefore, flow cytometry (FCM) emerges as a valuable and cost-effective tool bridging conventional laboratory testing and genetic testing. It provides a rapid and accurate results based on single-cell analysis (12).

Several authors have described the role of FCM in the diagnosis of IEIs; however, few have presented experiences from specific centers, and none from the Maghreb region (13–15). In this study, we retrospectively reviewed the use of FCM in the diagnosis of IEIs at a single center in Algeria, emphasizing the experiences of a diagnostic approach adapted to various clinical scenarios.

The study was conducted at the department of medical biology in Rouiba hospital and received ethical approval from the local committee in accordance with the Declaration of Helsinki. The study was designed as a retrospective review of FCM’s use in diagnosing IEIs over a nearly seven-year period, from May 2017 to February 2024. All patients met the updated criteria of the European Society for Immunodeficiency (ESID) (www.esid.org) and were categorized according to the updated classification of the IUIS expert committee (1).

The department of medical biology at Rouiba hospital is one of the few centers in Algeria offering a comprehensive workup for patients with suspected IEI. In this study, we conducted a retrospective review of the use of FCM in the diagnosis of IEIs. Sharing insights into our practical experiences, we present FCM-based diagnostic approaches adapted to different clinical scenarios. In the past two decades, FCM has emerged as a highly valuable and versatile tool for diagnosing and studying IEIs (12). Individual FCM diagnostic testing depends on the patient’s clinical presentation and basic laboratory findings. The range of applications is broad, including phenotypic assays that investigate the numbers and percentages of immune cells (e.g., RTE, TCRαβ DNT, switched memory B cells), functional analysis of cellular processes (e.g., cytokine secretion, STAT phosphorylation, NK cell degranulation, NADPH oxidase activity in neutrophils), and direct analysis of potentially mutated cell membrane, and cytoplasmic proteins ( Table 1 ) (9, 12, 19). The use of this diverse array of phenotypic and functional assays has proven instrumental in establishing definitive diagnoses for various disorders. However, it is important to note that these assays, if not conducted with meticulous attention to preanalytical and analytical considerations, may produce inaccurate results, potentially leading to misdiagnosis (12, 13, 20, 21). To implement such diagnostic assays with high and reproducible quality, a high level of expertise is required. Specifically, for protein expression and functional assays, in-house reference values need to be determined, and the parallel testing of healthy controls is highly recommended (9).

Over a nearly seven-year period, our laboratory diagnosed a total of 670 patients, with 70 different IEIs categorized into 9 different groups according to the IUIS classification ( Tables 2 , 3 ). FCM has proven useful in diagnosing and categorizing IEI in 514 patients (76.7%). Based on our experience, FCM provided direct diagnostic insights for immunodeficiencies such as SCID, OS, MHC-II deficiency, WAS, XLA, XL-CGD, FHL, LAD-1, AR-CD46 deficiency, and CD55 deficiency. For certain IEIs, including HIES, STAT1-GOF, APDS, ALPS, IPEX, AR-CGD, and autosomal dominant (AD)-CD46 deficiency, FCM offered suggestive evidence, necessitating subsequent genetic testing for confirmation. FCM findings provided informative clues, although they lacked specificity for diagnosing hypomorphic V(D)J recombination defects, Ataxia Telangiectasia, CVID, LRBA deficiency, RIPK1 deficiency, and Good’s syndrome ( Table 4 ). Additionally, in select IEIs such as selective IgA deficiency, transient hypogammaglobulinemia of infancy, congenital neutropenia, and early-onset inflammatory bowel disease, FCM played a crucial role in differential diagnosis and narrowing down possibilities.

In our laboratory, the flow cytometric analysis is systematically structured according to the clinical scenario ( Figure 6 ). For patients presenting with suspected CID, characterized by early onset severe/recurrent bacterial, fungal, and viral infections, along with severe reactions to live microorganism vaccines, the flow cytometric work-up starts with basic lymphocyte phenotyping. The identification of profound T lymphopenia (i.e., T cell count <300/μl) serves as a direct clue for the diagnosis of SCID (22). Simultaneous analysis of B and NK cell subpopulations aids in classifying SCID and facilitates the optimization of genetic testing through targeted Sanger sequencing. In our series, 30 out of the 163 patients (18.4%) diagnosed with combined immunodeficiency had SCID with a notable 70% exhibiting T-B-NK+ SCID. In contrast to patients with typical SCID, those harboring hypomorphic variants in SCID-causing genes typically manifest a less pronounced lymphopenia, thereby necessitating a more detailed analysis of naïve and memory subpopulations. Interestingly, despite substantial number of circulating T cells, naive T cells were absent in all patients with OS diagnosed in our laboratory ( Table 2 ). Delving into the analysis of naive T cells, and more interestingly RTE in these patients, provides compelling evidence for an accurate diagnosis ( Figure 6 ) (23). In addition to OS, leaky SCIDs form another category within the hypomorphic SCID spectrum. Patients with leaky SCID most often present with atypical phenotype characterized by less severe-but atypical-infections with a later onset, and a high frequency of immune dysregulation features, particularly autoimmune cytopenia (2, 24–26). T-B-NK enumeration in these patients often reveals slightly decreased to normal T-cell count, leading to a misdiagnosis (24). We previously reported a case of hypomorphic Artemis deficiency in a young adult, characterized by recurrent pneumonia, epidermodysplasia verruciformis, and a subtle T cell lymphopenia coupled with severe decrease in RTE (27). Given the diagnostic challenges inherent to such atypical entities, it appears reasonable to incorporate detailed T-cell phenotyping as a first-line investigation, optimizing diagnostic accuracy and minimizing the potential for misdiagnosis.

One hundred sixteen patients were diagnosed with bona fide CID. The most common immunophenotypic features were CD4 lymphopenia (78.4%), inverted CD4/CD8 ratio (64.7%), and reduced naïve CD4 (84.4%) and CD8 (87.8%) T cells. In addition to quantitative abnormalities, lymphocyte phenotyping can unveil subtle aberrations, such as low expression of CD3 complex on the T-cell surface. Indeed, we observed in two siblings, both affected with CD3γ deficiency, a 10-fold lower expression of the TCR-CD3 complex on the surface of T cells. Both brothers exhibited a clinical picture of CID with recurrent infections, autoimmune cytopenia, and lymphopenia. In view of these findings, FCM analysis should also carefully consider the intensity of CD3 expression.

MHC class II deficiency was the most prevalent CID in our series, accounting for 21.5% of all CIDs and 5.2% of total IEI cases. Genetic analysis conducted in 12 patients revealed pathogenic variants in the RFXANK gene in 10 patients, with 9 displaying an identical 26-base pair deletion (752delG26). The high frequency of the 752delG26 deletion in our patients is an accordance with earlier findings, underscoring a high occurrence of the same variant in over 90% of North African families (28, 29). This finding appears to be associated with a founder effect related to this mutation, as previously suggested by Ouederni et al. (30). Typically, the suspicion of MHC-II deficiency is raised in patients with recurrent infections, hypogammaglobulinemia, CD4 lymphopenia, and low CD4/CD8 ratio (31). However, it is noteworthy to mention that patients with MHC-II deficiency may paradoxically manifest a normal CD4+ T-cell count and serum immunoglobulin (Ig) levels. Notably, in our series, 11.4% of MHC-II deficient patients displayed a normal CD4+ T-cell count, 25.7% had normal CD4/CD8 ratio, and 45.7% maintained IgG concentration within the normal range ( Table 2 ). Hence, it would be relevant to systematically screen patients of North African descent exhibiting a clinical presentation of CID for MHC-II deficiency, particularly in cases involving parental consanguinity, recurrent pneumonia, and chronic diarrhea.

Hyper IgE syndrome is a heterogeneous group of monogenic disorders characterized by the triad of high serum IgE levels, eczema, and recurrent skin and lung infections. AD-HIES caused by LOF mutations in STAT3 gene is the prototype of these disorders. Additionally, six recently identified disorders (IL6ST, IL6R, ZNF341, ERBIN, CARD11, and TGFBR deficiencies) were included in the HIES spectrum, along with PGM3 and SPINK5 deficiencies (1, 32). Interestingly, high total IgE levels have also been reported in association with some CIDs, including DOCK8 deficiency, WAS, and ARPC1B deficiency (33–36). Accordantly, a crucial initial step in our approach for patients with suspected HIES is to rule out the presence of CID through a comprehensive T-cell phenotyping. Low Th17 cells and defective IL-17 production are characteristic features of patients with STAT3 deficiency and STAT3−related disorders, such as gp130, IL-6R, ZNF341, and PGM3 deficiencies (37–43). The analysis of CXCR3 and CCR6 markers on CD4+ memory T cells provides a straightforward approach for investigating Th17 lymphocytes (18). However, based on our experience, this phenotypic assay seems to be less sensitive in detecting impaired Th17 immunity compared to IL-17 production assay ( Table 2 ). Furthermore, patients with certain disorders sharing clinical features with HIES, such as IL-21R deficiency, may exhibit diminished Th17 cells (44). The STAT3 phosphorylation assay, originally designed for STAT3 deficiency, does not consistently exhibit abnormalities in cases of STAT3 deficiency depending on the mutation’s location (45). Moreover, other STAT3-related HIES frequently manifest a defective STAT3 phosphorylation (39–43).

In the context of suspected primary antibody production defect, the flow cytometric work-up usually starts with T-B-NK enumeration ( Figure 6 ). The absence or extremely low levels of B cells (<2% of circulating lymphocytes), accompanied by a significant reduction in total serum Ig levels and normal T cells strongly indicate a diagnosis of Agammaglobulinemia. Male patients with Agammaglobulinemia should be systematically screened for XLA-Btk deficiency. Since the introduction of the Btk expression assay in our laboratory, two patients have been tested and exhibited markedly decreased Btk expression in monocytes. According to the literature data, the majority of identified Btk mutations impair or abrogate Btk protein expression (9, 46). However, normal Btk protein levels do not exclude XLA, and in cases where clinical suspicion is high, genetic analysis should be performed (9, 15). Beyond the scope of Agammaglobulinemia, most patients with PAD display a less severe B lymphopenia, necessitating further B cell subset analysis ( Table 1 , Figure 6 ). Disturbances in B-cell subpopulations, such as reduced memory B cells as well as elevated transitional or CD21^lo B cells, provide direct insights into a primary origin for the antibody production defect, especially in adult patients where secondary causes of hypogammaglobulinemia are common (47).

Common variable immunodeficiency was the most prevalent PAD in our series, accounting for 45.2% of all PADs and 10.4% of total IEI cases. CVID is a heterogenous group of monogenic disorders characterized by reduced serum levels of IgG, IgA, and/or IgM, with impaired antibody response to both polysaccharide and protein antigens. Clinically, CVID patients suffer from an increased susceptibility to sinopulmonary infections along with a broad spectrum of inflammatory, granulomatous, and autoimmune diseases (48–50). Reduced numbers of switched memory B-cells, found in 80.9% of our CVID patients, represent the cellular hallmark of CVID and serve as a key diagnostic element according to ESID criteria (51, 52). Additional B-cell disturbances like expansion of transitional or CD21^lo B cells were also found in our CVID cohort. Genetic analysis through WES identified two patients with heterozygous GOF mutations in PIK3CD. Both patients presented with APDS symptoms (i.e., sinopulmonary infections, lymphoproliferation, enteropathy, and autoimmune hemolytic anemia), and displayed a characteristic immune profile including increased proportion of transitional B cells, reduced memory B cells, decreased naïve T cells, and elevated levels of serum IgM (53, 54).

A significant number of patients with CVID present some features of a cellular immunodeficiency and may, in fact, suffer from a certain form of CID (55). To exclude severe T-cell deficiency in patients with suspected CVID, we systematically conducted a comprehensive analysis of naïve and memory T-cell subpopulations. The revised criteria from the ESID registry require therefore a T-cell count of >200/μl with an amount of at least 10% of naïve CD4+CD45RA+ T cells in adults (52). von Spee-Mayer et al, reported that a reduction in naïve CD4 T cells to less than 10% had the highest sensitivity of all tested markers for patients with clinical complications often associated with CID, although the authors admitted that none of the current definitions sufficiently separates CID from CVID patients (56). It is noteworthy that both patients with APDS, as well as the one with TACI deficiency, exhibited a significant reduction in naïve CD4 T cells (less than 5%), despite being categorized as CVID.

Forty-nine patients were diagnosed with immune dysregulation disorders, including seven with FHL, five with ALPS, and three with LRBA deficiency. Elevated TCRαβ DNT represent a distinctive feature of ALPS and serve as a major diagnostic criterion (57). However, according to our experience, elevated TCRαβ DNT may also be found in other monogenic disorders, such as LRBA, ARPC1B, RIPK1, and SASH3 deficiencies, as well as CVID, and CMC with STAT1-GOF. Therefore, in addition to the clinical presentation, the extent of the expansion of TCRαβ DNT cells should be considered in the differential diagnosis, given that TCRαβ DNT cell elevation appears to be more pronounced in patients with ALPS. FCM analysis of patients with LRBA deficiency revealed a significant reduction in switched memory B cells and expanded CD21^lo B cells in all cases. While these B-cell disturbances are not specific, they offer valuable insights for diagnosing LRBA deficiency in patients with recurrent infections, features of immune dysregulation, and hypogammaglobulinemia (58).

Flow cytometric detection of perforin in NK cells has proven to be a rapid and sensitive test for identifying perforin deficiency (20). Typically, NK cells from patients with perforin deficiency exhibit either absent or markedly decreased perforin expression (20, 59). However, it is important to note that normal perforin expression does not definitively exclude defects in function or the presence of structurally abnormal proteins (20). In our laboratory, we have identified a patient displaying a perforin expression defect indicative of potential perforin deficiency. Remarkably, this investigated patient exhibited a strikingly normal percentage of positive NK cells (97%). However, he displayed a MFI that was notably tenfold lower compared to the control. Therefore, in the analysis of perforin expression, a comprehensive assessment should consider both the frequency and MFI for a more nuanced interpretation (60).

The DHR assay, measuring the NADPH oxidase activity, is a rapid and sensitive screening test for CGD (61). This assay can differentiate between X-CGD and AR-CGD (mainly caused by p47^phox defect) on the basis of distinctive DHR histogram SI and pattern (62–64). Vowells et al. demonstrated that the geometric mean SI from patients with CGD with defective gp91 ^phox and p47 ^phox were 1.3 (range, 0.9 to 2.2) and 13.2 (range, 3.5 to 52.1), respectively (61). In our cohort, three male patients displayed a complete lack of oxidative activity, indicating an XL form, while an additional four patients manifested a modest deviation in the DHR histogram with a SI ranging from 6 to 54, suggestive of a potential AR form. However, it is worth mentioning that this dichotomy is not consistently observed, as certain autosomal recessive forms, especially p22^phox deficiency, may present with null oxidase activity, and patients with confirmed X-CGD may exhibit a residual oxidase activity. In such instances, conducting the DHR assay for potential carriers (mothers) is crucial to identify mosaicism and predict the pattern of disease inheritance, although XL-CGD cases due to de novo mutation have been reported (65, 66). Additionally, further flow cytometric analysis of NADPH oxidase enzyme subunits (i.e., gp91^phox, p22^phox, p47^phox, and p67^phox) in patients and carrier mothers is useful in predicting the defective gene and enabling targeted genetic sequencing, thereby allowing for a rapid and cost-effective diagnosis of CGD (66).

Based on our experience, FCM analysis has proved to be effective in diagnosing CD46 and CD55 deficiencies. Among patients with aHUS, seven exhibited either absent or significantly decreased CD46 expression suggesting both AR and AD forms of the disease. In addition, six patients presenting with PLE displayed complete loss of CD55 expression, all of whom carried biallelic mutations in the CD55 gene. In the context of surface complement protein detection, both percentages and MFI of CD46/CD55 expression were analyzed. The reference MFI values were established from a group of 10 healthy subjects. Homozygous-deficient patients exhibited MFIs comparable to the isotypic control, whereas those with heterozygous deficiency demonstrated an MFI approximately 50% of the mean MFI of the control group. Unlike some X-linked disorders such as WAS, XLA, and X-linked HIMS, CD46/CD55 heterozygous carriers did not display a bimodal expression pattern on their leukocytes. Rather, they exhibited monomodal distribution with intermediate fluorescence intensity compared with healthy controls (67–69) ( Figure 5 ).

Over the past 7 years, our laboratory has enthusiastically embraced a diverse array of phenotyping and functional assays. While significant progress has been made, we recognize that there remain areas where our testing capabilities can be further enhanced. Therefore, we are dedicated to enhancing our testing panel by introducing new phenotypic and functional assays. Specifically, we aim to incorporate new markers such as TCRα signature (TCR Vα7.2) in V(D)J recombination defects, CD57 as a marker of senescent lymphocytes in APDS, gp91^phox, p22^phox, p47^phox, and p67^phox in CGD, as well as signaling lymphocyte activation molecule (SLAM)–associated protein (SAP) and X-linked inhibitor of apoptosis protein (XIAP) in X-linked lymphoproliferative diseases 1 and 2 (12, 66, 70, 71). Additionally, we aspire to integrate lymphocyte proliferation assays into routine practice and develop new functional tests, such as the assessing of histone H2AX phosphorylation (γH2AX) in CD3 T cells following irradiation, commonly used in diagnosing Ataxia Telangiectasia and radiosensitive SCIDs (72, 73).

Flow cytometry has emerged as a highly valuable and cost-effective tool for diagnosing and studying IEIs, particularly in low-income countries where access to genetic testing is limited. A stratified flow cytometric analysis of specific proteins or particular subpopulations plays a crucial role in establishing definitive diagnoses for various disorders. While molecular testing remains necessary for a definitive diagnosis in some IEI disorders, conducting phenotypic or functional analyses of lymphocyte subsets provides clues to the underlying genetic defects or aids in narrowing down the list of putative genes to be analyzed.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: PRJNA1089953 (SRA).

The studies involving humans were approved by Ethics committee of Rouiba Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

AT: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Validation, Writing – original draft. RBe: Data curation, Writing – review & editing. AY: Data curation, Investigation, Writing – review & editing. SH: Data curation, Writing – review & editing. FF: Data curation, Writing – review & editing. MK: Data curation, Writing – review & editing. HBe: Data curation, Writing – review & editing. ST: Data curation, Writing – review & editing. SA: Data curation, Writing – review & editing. JS: Data curation, Writing – review & editing. JN: Data curation, Writing – review & editing. FZ: Data curation, Writing – review & editing. SM: Data curation, Writing – review & editing. RA: Writing – review & editing. AS: Writing – review & editing. YF: Writing – review & editing. AK: Writing – review & editing. HMe: Writing – review & editing. TB: Data curation, Writing – review & editing. ZBe: Data curation, Writing – review & editing. AD: Writing – review & editing. KO: Writing – review & editing. FA: Data curation, Investigation, Writing – review & editing. MF: Writing – review & editing. CBe: Data curation, Writing – review & editing. RK: Data curation, Writing – review & editing. AO: Data curation, Writing – review & editing. AS: Writing – review & editing. IB: Writing – review & editing. CBo: Writing – review & editing. NBo: Writing – review & editing. IM: Writing – review & editing. NK: Writing – review & editing. HBo: Software, Writing – review & editing. TK: Writing – review & editing. FM: Writing – review & editing. MBou: Writing – review & editing. AZ: Writing – review & editing. OG: Writing – review & editing. ML: Writing – review & editing. AM: Writing – review & editing. NBe: Writing – review & editing. MBen: Writing – review & editing. ZZ: Writing – review & editing. BB: Data curation, Investigation, Writing – review & editing. MuB: Writing – review & editing. BA: Writing – review & editing. ZBo: Writing – review & editing. OI: Writing – review & editing. HMa: Data curation, Investigation, Writing – review & editing. LK: Data curation, Investigation, Writing – review & editing. LS: Writing – review & editing. RBouk: Supervision, Writing – review & editing. CL: Data curation, Investigation, Writing – review & editing. SR: Data curation, Investigation, Writing – review & editing. LN: Data curation, Investigation, Writing – review & editing. KD: Data curation, Investigation, Writing – review & editing.