Identification ofHLA-A,HLA-BandHLA-Ctriple homozygous and double homozygous donors: a path towards synthetic superdonor Advanced Therapeutic Medicinal Products

Identification of HLA-A, HLA-B and HLA-C triple homozygous and double homozygous donors: a path towards synthetic superdonor Advanced Therapeutic Medicinal Products | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Identification of HLA-A , HLA-B and HLA-C triple homozygous and double homozygous donors: a path towards synthetic superdonor Advanced Therapeutic Medicinal Products View ORCID Profile Daniel Naumovas , View ORCID Profile Barbara Rojas-Araya , View ORCID Profile Catalina M. Polanco , View ORCID Profile Victor Andrade , View ORCID Profile Rita Čekauskienė , View ORCID Profile Beatričė Valatkaitė-Rakštienė , View ORCID Profile Inga Laurinaitytė , View ORCID Profile Artūras Jakubauskas , View ORCID Profile Mindaugas Stoškus , View ORCID Profile Laimonas Griškevičius , View ORCID Profile Ivan Nalvarte , View ORCID Profile Jose Inzunza , View ORCID Profile Daiva Baltriukienė , View ORCID Profile Jonathan Arias doi: https://doi.org/10.1101/2025.05.19.25327154 Daniel Naumovas 1 Vilnius University Life Science Center EMBL partnership institute for gene editing technologies, Laboratory of nuclease enabled cell therapies 2 Doctoral program of Biology, Vilnius University Life Science Center 3 Vilnius Santaros Klinikos Biobank, Vilnius University Hospital Santaros Klinikos , Vilnius, Lithuania 4 Department of Molecular Medicine; Hematology, Oncology and Transfusion Medicine Center, Vilnius University Hospital Santaros Klinikos , Vilnius, Lithuania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel Naumovas Barbara Rojas-Araya 1 Vilnius University Life Science Center EMBL partnership institute for gene editing technologies, Laboratory of nuclease enabled cell therapies Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Barbara Rojas-Araya Catalina M. Polanco 1 Vilnius University Life Science Center EMBL partnership institute for gene editing technologies, Laboratory of nuclease enabled cell therapies Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catalina M. Polanco Victor Andrade 5 Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne , 50937 Cologne, Germany 6 Department of Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn , Bonn, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Victor Andrade Rita Čekauskienė 4 Department of Molecular Medicine; Hematology, Oncology and Transfusion Medicine Center, Vilnius University Hospital Santaros Klinikos , Vilnius, Lithuania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rita Čekauskienė Beatričė Valatkaitė-Rakštienė 4 Department of Molecular Medicine; Hematology, Oncology and Transfusion Medicine Center, Vilnius University Hospital Santaros Klinikos , Vilnius, Lithuania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Beatričė Valatkaitė-Rakštienė Inga Laurinaitytė 3 Vilnius Santaros Klinikos Biobank, Vilnius University Hospital Santaros Klinikos , Vilnius, Lithuania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Inga Laurinaitytė Artūras Jakubauskas 4 Department of Molecular Medicine; Hematology, Oncology and Transfusion Medicine Center, Vilnius University Hospital Santaros Klinikos , Vilnius, Lithuania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Artūras Jakubauskas Mindaugas Stoškus 4 Department of Molecular Medicine; Hematology, Oncology and Transfusion Medicine Center, Vilnius University Hospital Santaros Klinikos , Vilnius, Lithuania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mindaugas Stoškus Laimonas Griškevičius 4 Department of Molecular Medicine; Hematology, Oncology and Transfusion Medicine Center, Vilnius University Hospital Santaros Klinikos , Vilnius, Lithuania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laimonas Griškevičius Ivan Nalvarte 7 Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, BioClinicum , Visionsgatan 4, Solna, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ivan Nalvarte Jose Inzunza 8 Karolinska Institutet Stem Cell Organoid (KISCO) facility, Department of Laboratory Medicine , Alfred Nobels allé 8, Huddinge, Sweden 9 Karolinska Institutet, Department of Laboratory Medicine , Alfred Nobels allé 8, Huddinge, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jose Inzunza Daiva Baltriukienė 10 Department of Biological Models, Institute of Biochemistry, Life Sciences Center, Vilnius University , Vilnius, Lithuania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daiva Baltriukienė Jonathan Arias 1 Vilnius University Life Science Center EMBL partnership institute for gene editing technologies, Laboratory of nuclease enabled cell therapies 8 Karolinska Institutet Stem Cell Organoid (KISCO) facility, Department of Laboratory Medicine , Alfred Nobels allé 8, Huddinge, Sweden 9 Karolinska Institutet, Department of Laboratory Medicine , Alfred Nobels allé 8, Huddinge, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonathan Arias For correspondence: jonathan.arias{at}gmc.vu.lt Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Immune matching and rejection pose major hurdles in tissue transplantation. Here, we profile HLA-A , HLA-B , and HLA-C alleles in 3,496 Lithuanian donors genotyped at three-field resolution. The five most frequent alleles constitute 74.6% of HLA-A , 43.2% of HLA-B , and 59.2% of HLA-C , with HLA-A*02:01:01, HLA-B*07:02:01, and HLA-C*07:02:01 being the most common. Lithuanian allele frequencies closely resemble those of populations with pre-Neolithic hunter-gatherer ancestry, such as European-American and British groups. We identified 153 double homozygotes and 51 triple homozygotes for HLA-A , HLA-B , and HLA-C . Compatibility modeling showed triple homozygous profiles match 60.5% of Lithuanians (33.3% for double homozygotes), 13.4% of British population, and 7.4% of European-Americans. CRISPR-Cas9 guide RNA design yielded 54 candidates predicted to disrupt HLA-A or HLA-B , while preserving HLA-C , producing edited profiles matching over 98.1% of Lithuanians, 95.8% of European-Americans, and 95.6% of British population. Finally, we established 16 fibroblast lines from double and triple homozygotes, offering a resource for immune-compatibility studies and regenerative medicine applications. 2. Introduction Transplantation of allogeneic organs, tissues and cells are constrained by immune-matching between the graft and the host. Immune matching is mediated by the human leukocyte antigen (HLA) genes. They are clustered in a 3.7M bp locus on chromosome 6, highly polymorphic and their inheritance reported as having intermediate linkage disequilibrium 1 , 2 . Recent adoption in clinic of high resolution haplotyping, have improved the accuracy of immune matching for the more than 38,000 HLA alleles cataloged in the IPD-IMGT/HLA Database 3 . Pursuing high level of matching is intended to minimize adverse events, such as graft-versus-host disease (GVHD) or immune rejection, which are frequently managed with immune suppressive drugs. There is a broad assortment of immune suppressive treatments for the management of transplantation, encompassing small molecule inhibitors, antimetabolites, corticosteroids, and antibodies 4 – 6 . However, immune suppressive therapies are linked to an increased risk of infection 7 , 8 . Therefore, pursuing high level of matching is intended to minimize adverse events caused by immune rejection and immune suppression. The importance of a high degree of immune matching for improving survival rates is well documented in the literature 9 . Indeed, high levels of matching, referred as a complete HLA match 10 , has a positive impact on patient survival. HLA class I homozygous individuals offer increased immune compatibility with a relatively larger base of the population. They are individuals very scarcely represented in the population as expected from mendelian ratios. Cells from naturally occurring triple and double homozygous individuals are very valuable for the study of immune compatibility and applications of regenerative medicine. Genome editing tools are currently used to engineer synthetic immune compatibility, also called hypo-immunogenicity, in tissues and cells. This aid overcoming the challenges of identifying rare haplotypes in donor pools. Several approaches have been developed to bypass immune recognition by cytotoxic T cells while retaining self-recognition mediated by NK cells. The most frequent strategies through loss-of-function include the knock-out of specific HLA class I 11 and class II genes, beta-2-microglobulin ( B2M ) 12 , 13 , CIITA 14 , TAP1 or TAP2 and CD74 15 . Conversely, the most frequent gain-of-function strategies involve the knock-in of CD47 and HLA-E 16 . Pioneering studies have demonstrated that gene editing depletion of HLA-A and HLA-B genes preserves the host NK cell recognition while preventing the CD8 T-cell mediated host-versus-graft rejection 17 . This approach yields cells currently known as HLA-C retained. Triple and double homozygous samples are the ideal cell source for modulating immunogenicity, as they start from a relatively higher level of immune compatibility. Furthermore, they can be engineered in their HLA genes using programmable nucleases through simpler strategies compared to heterozygous samples. In this study we identify a cohort of naturally occurring triple and double homozygous individuals and isolated primary samples for prospective regenerative medicine applications. Additionally, we conducted an analysis of the frequency of class I HLA genes in the European-Lithuanian population, specifically characterizing the HLA-A , HLA-B and HLA-C haplotypes in a cohort of 3,496 individuals. Genetic makeup of Lithuania population is located within a European context, influenced by pre-Neolithic Western and Scandinavian hunting-gathering groups, early to middle Bronze Age steppe pastoralists, late Neolithic Bronze Age Europeans, and largely sheltered 18 , which make it from an immune compatibility standpoint closely resembling European-American 19 , and British populations 20 . We compared this population to publicly available datasets of European ancestry and modeled the impact of gene editing on immune matching and population coverage, specifically in a pan-European context. 3. Materials and methods Ethical approval This study is part of the ethical approval 2023/6-1524-984 “Highly-immune compatible iPS cells as source for Regenerative Medicine and Cell Therapy-oriented applications” from the Vilnius Regional Biomedical Research Ethics Committee to Vilnius University and 2023/4-1507-968 “Analysis of the Distribution of Human Leukocyte Antigen (HLA; Encoding Genes - HLA) Alleles and Haplotypes in the Group of the Lithuanian Unrelated Bone Marrow Donor Registry” to Vilnius University Hospital Santaros Klinikos. Written consents were obtained from the participants of the study. Genotyping HLA typing for Registry donors’ peripheral blood was performed at the EFI accredited Immunogenetics laboratory at Vilnius University Hospital (Vilnius, Lithuania) using sequencing-based typing and at the ASHI accredited laboratory, HistoGenetics (Ossining, NY), using next-generation sequencing. Exons 2 and 3 for class I HLA were covered. Fibroblasts derivation and genotyping Skin samples were collected using a 2-3 mm biopsy punch needle, and fragmented with a sterile scalpel and needle. Fibroblasts were grown with AmnioPrime Complete Medium (Capricorn Scientific cat no. APR-B), supplemented with Amphotericin B (Capricorn Scientific cat. no. AMP-B) for 21 to 45 days until fibroblast migrate from tissue sections and reach 80-90% confluence. The medium was regularly changed every 3 days to ensure optimal cell growth. Fibroblasts were routinely passaged with 0.25% Trypsin-EDTA at a density of 2 x 10 5 cells/cm 2 . Genomic DNA from fibroblasts was purified using DNeasy blood and tissue kit (Qiagen cat no. 69504) and genotyped using the primers HLAA-P1: TCCAGGTGGACAGGTAAGGA, HLAA-P2: GTCACTGCCTGGGGTAGAAC, HLAB-P1: TGCATTCTGGGTTTCTCTACTGG, HLAB-P2: CACGCGAAACATCCCAATCA, HLAC-P1: AGGTAAGGCAAAGGGTGGGA, HLAC-P2: AGGCCGCCTGTACTTTTCTC. Samples were Sanger sequenced using the primers HLAA-P3: ACCCTCGTCCTGCTACTCTCG, HLAB-P3: ACCCTCCTCCTGCTGCTCTG, HLAC-P3: CGTTGGGGATTCTCCACTCC at Microsynth. Bioinformatics Python and R scripts used for data analysis are made available thorough Supplementary Data and the open-source GitHub developer platform. Quantification of HLA allele frequency in the population The total allele count in the dataset was divided by number of alleles (n=2) times the number of individuals in this study (n=3,496) having at least third-field resolution. Hardy-Weinberg Equilibrium (HWE) analyses The observed genotypes present in the population were quantified (n=3,496). The allele frequencies were determined using sampled genotype count and the expected genotype frequencies calculated. The observed and expected genotype counts are compared with a X 2 test. The X 2 test is reliable for genotypes present more than 5 times in the population. Genotypes with count <5 times were filtered from the HWE analyses. The degrees of freedom df=(n(n+1)/2)−n are estimated in function of the number of possible genotypes and alleles number identified in the sampled population for each HLA class I gene, 44 for HLA-A , 83 for HLA-B and 45 for HLA-C . Regression analysis Allele frequencies were extracted from the publicly available data from European-American 19 , British 20 and African-American populations 21 , and compared to the allele frequency from our study. Linear regression analyses y ∼ mx + c was performed using R for pairwise comparison of allele frequencies of HLA-A , HLA-B and HLA-C . Frequencies are calculated as frequency = allele count(dataset) / n(dataset). Principal component analysis Monte Carlo population haplotypes were simulated based on the published allele frequencies of European-American, British and African-American cohort studies. Data was processed with one-hot encoding to convert allele entries per individual into 1 or 0, using the caret library from R 22 . Centroids and Euclidean distances were calculated from the principal components. Distances were represented as edges and as heatmaps. HLA sequence analysis and sgRNA activity prediction The sequences for all alleles in protein, transcript and gene level were downloaded as fasta files from IPD-IMGT-HLA database 23 and analyzed in python and R. Allele sequences were extracted based on the HLA alleles present in the population. Cas9 binding sites were extracted with python and analyzed in R using CrisprScore 24 . Transmembrane prediction was conducted with DeepTMHMM 25 . 4. Results Analysis of the HLA class I frequency in Lithuanian population The Lithuanian Bone Marrow Donor Registry, located at Vilnius University Hospital Santaros Klinikos, includes 13,884 individuals, with 11,153 characterized at second field (protein level) for HLA-A , HLA-B and HLA-C . Of these, 3,496 individuals are characterized at third field ( Figure 1A ). We found that 858 individuals are at least homozygous for one HLA class I gene. A total of 542 individuals are homozygous for the coding sequence of HLA-A , 233 individuals are homozygous for HLA-B , and 338 individuals are homozygous for HLA-C ( Figure 1B ). We confirmed the Lithuanian population closely reassemble HLA distribution of the most frequent European HLA class I alleles ( Table 1 and Figure 1 ). The HLA types identified and their prevalence in the population are summarized in Table 1 . The five most frequent HLA-A alleles are A*02:01:01, A*03:01:01, A*24:02:01, A*01:01:01 and A*11:01:01 which account for 74.6% of the population ( Figure 1C ). Noteworthy is the fact that HLA-A*02:01:01 is the most frequent HLA class I allele with a representation of 31.6% in the population. Similarly, the five most frequent HLA-B alleles are B*07:02:01, B*13:02:01, B*15:01:01, B*44:02:01 and B*40:01:01 which account for a 43.2% of the population ( Figure 1D ). HLA-B*07:02:01 alone represents 15.1% of the Lithuanian population. Furthermore, the five most frequent HLA-C alleles are C*07:02:01, C*06:02:01, C*04:01:01, C*02:02:02 and C*07:01:01 with a cumulative frequency of 59.2% of the population ( Figure 1E ). Download figure Open in new tab Figure 1. A. Dataset structure from this study B. Proportional Venn diagram showing the prevalence of HLA class I homozygous individuals in the Lithuanian population, with the composition of double homozygous and triple homozygous individuals highlighted. The most common HLA alleles with a frequency above 0.01, are shown for C. HLA-A , D. HLA-B , and E. HLA-C . View this table: View inline View popup Table 1. HLA class I allele frequencies observed in the Lithuanian population (n = 3,496) It is important to highlight that the HLA-B gene exhibits the largest diversity of alleles, followed by HLA-A and HLA-C ( Table 1 ), as also observed in previous studies on populations of European origin 19 , 20 . Comparisons of the Lithuanian Class I HLA frequencies with those reported for the European-American and British populations through linear regression models show strong correlations between the three cohorts ( Figure 2 ). The linear regression analyses show an average slope of 0.914 for HLA-A , 0.826 for HLA-B , and 0.860 for HLA-C . This indicates the populations closely resemble each other, regarding the composition and prevalence of allele variants. In order to validate this comparison we fitted a linear model with the HLA class I frequencies of African-American population 21 and confirmed the allele frequency differences as reported by regressions, with slope of 0.346 for HLA-A , 0.310 for HLA-B , and 0.611 for HLA-C ( Figure 2 ). Principal component analysis (PCA) of the genotypes of the Lithuanian population and those extracted from published datasets, reconstructed through Monte Carlo analysis based on reported allele frequencies, showed close proximity ( Figure 3A ). The Euclidean distances between the centroids of the populations were quantified and represented in the PCA and as a heatmap ( Figure 3B ). The distance metrics indicate that the centroid of the Lithuanian population is proximal to the European-American and British populations with distances of 2.49 and 2.95 relative units, respectively. The British and European-American population closely resemble each other with a Euclidean distance of 0.61. Conversely, the African-American population presents an average Euclidean distance of 5.72 relative units with the other datasets. Download figure Open in new tab Figure 2. Comparison of the HLA allele frequencies identified in the Lithuanian population with those reported in studies for the European-American population, the British population and the African-American population for A. the HLA-A transcript, B. HLA-B transcript and C. the HLA-C transcript. Reference lines with slope n=1 are represented in dashed grey. The linear regressions of frequencies on the scatter plots are represented with a red solid line, with the R 2 of the linear model and the slope indicated. Download figure Open in new tab Figure 3. A. Principal component analysis of the HLA class I distribution in the Lithuanian population (this study) and pan-European populations, including European-American, British, and African-American cohorts. The centroid of each population is marked with a circle. The Euclidean distances between the centroids were calculated, and their edges are plotted with solid lines. B. Euclidean distance heatmap between the studied populations. Blue correspond to higher Euclidean distances in the principal component space. Population analysis for HLA class I composition We conducted a Hardy-Weinberg equilibrium analysis to determine whether there were deviations in the genotype frequencies of the Lithuanian HLA class I allele pool compared to the expected frequencies under Hardy-Weinberg conditions for each allele. We found 44 alleles for HLA-A , 83 alleles for HLA-B , and 45 alleles for HLA-C within the 3,496 individuals analyzed ( Table 1 ). This diversity of allele types results in a maximum of 990 genotypes for HLA-A , 3,486 for HLA-B , and 1,035 for HLA-C . When excluding the rare variants, defined as those with a frequency of less than 0.01 in the population, we found that 22 genotypes deviate from Hardy-Weinberg equilibrium for HLA-A , 16 deviate for HLA-B , and 30 deviate for HLA-C ( Figure 4A ). The highest-ranked HLA-A genotype that is present more frequently than expected is HLA-A*03:01:01-25:01:01, which combines two of the top six most abundant allele types ( Figure 4D ). Likewise, HLA-B*13:02:01-27:05:02, HLA-B*08:01:01-15:01:01, and HLA-B*15:01:01-44:02:01, which are combinations of the top ten most frequent allele types, are present at higher frequencies than expected under Hardy-Weinberg equilibrium conditions ( Figure 4A ). Furthermore, a larger group of HLA-C genotypes deviate from equilibrium, including genotypes of the most frequent allele types HLA-C*07:01:01-12:03:01, HLA-C*01:02:01-04:01:01, HLA-C*02:02:02-06:02:01, HLA-C*04:01:01-06:02:01, HLA-C*03:04:01-12:03:01, HLA-C*01:02:01-06:02:01, and HLA-C*07:02:01-07:02:01 ( Figure 4A ). In fact, HLA-C*07:02:01 is the most frequent allele type, and its homozygous combination is enriched more than expected based on Hardy-Weinberg equilibrium conditions. Download figure Open in new tab Figure 4. Hardy-Weinberg equilibrium (HWE) analysis for A. HLA-A , HLA-B and HLA-C genotypes. The heatmaps represent the ratio between observed genotype frequencies and expected genotype frequencies. Only the genotypes were X 2 value exceeds the X 2 -threshold, indicating a HWE deviation, were filtered. The subset of genotypes with a frequency higher than 0.01 are represented in heatmaps. Identification of HLA class I double homozygous and triple homozygous individuals in the Lithuanian population Of the HLA homozygous individuals, a total of 153 are double homozygous ( Figure 1A and Table 2 ), 58 are double homozygous for HLA-A and HLA-B , 76 are double homozygous for HLA-A and HLA-C , and 172 are double homozygous for HLA-B and HLA-C . Remarkably, 51 individuals are triple homozygous for HLA-A , HLA-B , and HLA-C ( Figure 1A and Table 3 ). Haplotype frequencies of the complete dataset (3,496 individuals) are available in Supplementary Data. View this table: View inline View popup Table 2. HLA class I double homozygous haplotypes identified in this study (n = 153) View this table: View inline View popup Download powerpoint Table 3. HLA class I triple homozygous haplotypes identified in this study (n = 51) HLA class I immune compatibility cumulative-coverage based on stochastic sampling, double homozygous or triple homozygous sampling The cumulative-coverage accounts for the extent of redundancy in the population, which allows for matching even when a single sample may not be accessible for donation. Using the third-field HLA information for HLA-A , HLA-B and HLA-C from 3,496 individuals from this study, we conducted sampling simulations and calculated the cumulative coverage of 1,000 randomly selected individuals from the Lithuanian population. Using 100 simulations, we found that, on average, 1,000 randomly selected samples achieve a cumulative-coverage of 3.1 ± 0.4 times the population ( Figure 5A ). We found that approximately 329 individuals provide an average cumulative-coverage of 0.99 ± 0.2 of the populations ( Figure 5B ). We then excluded the possibility of autologous donation from the population’s cumulative-coverage analysis. When randomly selecting 1,000 individuals in 100 sampling iterations, a lower cumulative-coverage was achieved, with an average of 2.7 ± 0.4 times the population ( Figure 5C ). Sampling 329 individuals, excluding the possibility of autologous donation, results in a cumulative-coverage of only 0.9 ± 0.2 times the population ( Figure 5D ). Remarkably, when evaluating the cumulative-coverage of double or triple homozygous for HLA-A , HLA-B and HLA-C , a higher cumulative-coverage is achieved with a smaller set of samples. The 153 double homozygous samples achieve a cumulative-coverage of 2.2 times the population, and the 51 triple homozygous samples yield a cumulative-coverage of 4.9 times the population ( Figure 5E ). A side-by-side comparison of the double and triple homozygous cumulative-coverage with stochastic sampling of 1,000 individuals highlights the impact of homozygosity on immune compatibility to the population ( Figure 5E ). Download figure Open in new tab Figure 5. Cumulative-coverage of HLA class I immune matching in the Lithuanian population. Individuals in our dataset were sampled, and their cumulative-coverage in the population was calculated with 100 sampling iterations for A. 1,000 individuals, B. 329 individuals, which is the estimated sample size to reach a 1-time cumulative-coverage. C. Sampling of 1,000 and D. 329 individuals excluding the possibility of autologous donation. E. Cumulative-coverage for the individuals found to be double homozygous (red) and triple homozygous (blue) for HLA-A , HLA-B and HLA-C . Comparison with 1,000 randomly sampled individuals in 100 iterations (grey). The average cumulative coverage of all iterations is shown in black. Compatibility of HLA class I in the Lithuanian and pan-European populations We stochastically arranged the 3,496 donors and interrogated whether the subset of HLA-A , HLA-B and HLA-C triple homozygous (51 samples), and double homozygous (153 samples) were compatible with the 3,496 patients ( Figure 6A ). We found that our cohort of triple homozygous patients matches 60.46% of the Lithuanian population. Likewise, the double homozygous cohort matches 33.32% of the Lithuanian population. In comparison, a randomly selected subset of 153 or 51 samples of the dataset could match only 11.84% ( Figure 6B ) and 4.1% ( Figure 6C ) of the Lithuanian population, respectively. We then evaluated the matching provided by our triple homozygous and double homozygous cohorts to the pan-European population by assessing their immune compatibility with Monte Carlo datasets, reconstructed from the allele frequencies reported for European-American and British individuals. Remarkably, we found that the 51 triple homozygous samples of our cohort match 13.4% of the British population, while the double homozygous cohort matches 5.2% ( Figure 6D ). Additionally, we found that triple homozygous samples match 7.4% of the European-American population, and double homozygous samples match of 3.3% ( Figure 6E ). Download figure Open in new tab Figure 6. Population compatibility of HLA-A , HLA-B and HLA-C genotypes in Lithuanian samples with the Lithuanian and pan-European populations. Immune compatibility of triple homozygous (51 individuals), double homozygous (153 individuals) and A. all samples from the cohort of 3,496 individuals of this study, B. stochastically selected samples from 153 individuals, and C. stochastically selected samples from 51 individuals. Immune compatibility of HLA class I genes, HLA-A , HLA-B and HLA-C , in Lithuanian samples with D. British datasets and E. European-American datasets. The triple homozygous individuals are indicated in blue, double homozygous individuals in red, and stochastically selected subsamples in green. Cas9 activity prediction on HLA class I alleles in the Lithuanian population We extracted the Cas9 binding site sequences from the HLA alleles present in the Lithuanian population. First, we focused on the analysis of target regions encompassing the gene body from 5’UTR to the 3’UTR. We found 1,996 unique target sites in HLA-A , 2,342 unique target sites in HLA-B , and 2,300 unique target sites in HLA-C . We calculated the activity prediction score based on the rule set 1 of nuclease catalytic activity 26 . We found that, as in non-hyper polymorphic genes, the activity scores of all HLA alleles are centered in the non-active Q4 quadrant. We show this distribution for the five most frequent alleles of HLA-A , HLA-B and HLA-C ( Figure 7A ). The potential of HLA gene knock-out to modulate immune compatibility is well accepted in the literature. Although pairs of guide RNAs can be used in conjunction to create exon spanning knock-outs, we focused on guide RNA in exon regions. From the guide RNAs present in the gene body, we found 679 unique target sites in the HLA-A exons of Lithuanian alleles, 698 in HLA-B , and 687 in HLA-C ( Figure 7B ). Since HLA-A , HLA-B and HLA-C are class I single-span transmembrane proteins ( Figure 7C ), only guide RNAs targeting the ectodomain have the capacity to create knock-outs that eliminate plasma membrane expression of HLA genes. We predicted the transmembrane spanning region 25 of the allele sequences and focused on guide RNAs directed to the N-terminus, upstream of the predicted transmembrane domain. We found there are 615 unique target sites in Lithuanian alleles on HLA-A ectodomains, 658 on HLA-B and 613 on HLA-C ( Figure 7B ). Of those useful for ectodomain targeting, a fraction have predicted high activity score larger than 0.5. These include 54 for HLA-A , 75 for HLA-B, and 66 for HLA-C ( Figure 7B ). Download figure Open in new tab Figure 7. Predicted guide RNA sequence activity for the 5 most frequent alleles in the Lithuanian population for A. HLA-A , HLA-B and HLA-C . B. Nested distribution of guide RNAs on the gene body, exons, ectodomain and those with predicted high-activity for HLA-A , HLA-B and HLA-C . C. Protein structure models and gene structures for HLA-A, HLA-B and HLA-C. Protein structures are depicted as mature forms, excluding the signal peptide and without the highly flexible endodomain. Gene structure highlights the matching ectodomain and transmembrane (TM). Modeling the impact of HLA class I engineering on the immune compatibility of triple homozygous and double homozygous donor samples Naturally occurring triple and double homozygous samples are particularly useful for gene engineering approaches as they allow bi-allelic targeting with a single programmable nuclease in a one-step intervention. Next, we modelled the impact of HLA-A and HLA-B knock-out on the immune compatibility of the double and triple homozygous samples when matching them to the Lithuanian population and pan-European datasets ( Figure 8 ). We included all 51 triple homozygous individuals from our cohort ( Figure 8A ). From the 153 double homozygous individuals identified, we focused on those that are HLA-A and HLA-B double homozygous, comprising 7 individuals ( Figure 8B ). The 51 triple homozygous samples, when in an HLA-C retained ( HLA-A and HLA-B double knock-out) configuration, match a maximum of 0.9799 of the Lithuanian population ( Figure 8A ). These 51 samples achieve a match of 0.9577 in the European-American population ( Figure 8C ) and 0.9556 on the British population ( Figure 8D ). Download figure Open in new tab Figure 8. Population compatibility model of HLA-A and HLA-B double knock-out samples from our cohort with the Lithuanian and pan-European populations. A. Immune compatibility of the 51 triple homozygous individuals in an HLA-A and HLA-B double knock-out model, and B. the 7 double homozygous individuals in an HLA-A and HLA-B double knock-out model when matched to the Lithuanian population. Cohort from A when matched to the C. European-American dataset and D. the British dataset. Sampling of HLA-A , HLA-B and HLA-C triple homozygous individuals from the Lithuanian population Since the triple and double homozygous individuals identified in this study are immune-compatible with a large fraction of the Lithuanian and pan-European population, we recruited these volunteer donors to collect dermal fibroblast primary samples for establishing biobank stocks and cultures. Primary fibroblast cultures were robustly established for 16 triple and double homozygous individuals ( Figure 9A ). Sanger sequencing of the PCR products of exon 2 and exon 3, which code for the ectodomain of HLA-A , HLA-B , and HLA-C , revealed characteristic residues for each allele. Characteristic amino acids p.F33 and p.R121 were confirmed for HLA-A*02:01:01:01 ( Figure 9C ), p.Y33 and p.W119 for HLA-B*13:02:01:01 ( Figure 9D ), and p.D33 and p.L119 for HLA-C*06:02:01:01 ( Figure 9E ). These findings were consistent for both the XY donor (donor SD9) and XX donor (donor SD6) individuals with homozygous haplotypes HLA-A*02:01:01:01-HLA-B*13:02:01:01-HLA-C*06:02:01:01 ( Figure 9F ). Download figure Open in new tab Figure 9: A. Fibroblasts cultures from HLA-A , HLA-B and HLA-C triple homozygous donors. B. Genotyping PCR for HLA-A , HLA-B and HLA-C . Sanger sequencing analysis for C. HLA-A , D. HLA-B and E. HLA-C . F. Next generation sequencing haplotype for donor patient and linked fibroblasts. 5. Discussion Our study on allele and haplotype frequencies of the HLA-A , HLA-B and HLA-C genes in the Lithuanian population elucidates immune compatibility structure in relation to other European populations. Comparative analyses confirmed a high degree of similarity in HLA immune compatibility genes between the Lithuanian population and pan-European groups. The most frequent alleles described in the British 20 and European-American populations 19 are also the most frequent in the Lithuanian population, with frequencies of 31.7% (A*02:01:01), 5.3% (B*08:01:01), 15.0% (B*07:02:01), and 8.7% (C*07:01:01). Linear regression analysis using publicly available data corroborated these observations. Principal component analysis (PCA) and Euclidean distance calculations further confirmed the proximity in immune compatibility between Lithuanian, European-American, and British populations. Our Hardy-Weinberg equilibrium (HWE) analysis revealed deviations in a subset of alleles, suggesting partial genetic isolation or selective pressure. This finding aligns with previous studies indicating low levels of admixture and a significant component of pre-Neolithic hunter-gatherer ancestry in the Lithuanian group 18 . The majority of individuals in our registry (n = 11,153) were characterized at second-field resolution for HLA-A , HLA-B , and HLA-C , while a subset (n = 3,496) underwent third-field resolution analysis. This divergence reflects technological advancements in clinical registries, with long-read sequencing platforms now enabling fourth-field resolution 27 , 28 . Although our analyses do not encompass HLA class II, it is well established that its expression occurs in specialized immune cell lineages, while HLA class I primarily regulates non-immune cell compatibility. Remarkably we found a subset of 51 triple homozygous for HLA-A , HLA-B and HLA-C , and a subset of 153 double homozygous individuals. The proportion of triple-homozygous individuals exceeded stochastic expectations based on measured allele frequencies (2.99 ± 1.76), suggesting underlying population structures, as indicated by HWE analysis. Due to the significant immune compatibility provided by HLA-A , HLA-B and HLA-C triple homozygous individuals 29 , 30 the term naturally-occurring “ super donors ” has been proposed 31 . Our study identified 51 naturally-occurring super donors, who exhibit class-I immune matching with 60.54% of the Lithuanian population, 13.4% of the British population, and 7.4% of the European-American population. It is important to highlight that using triple homozygous samples for cell line development, particularly human induced pluripotent stem (iPS) cells, results in derivatives with wider immune compatibility than heterozygous counterparts. Genetic engineering with programmable nucleases in such samples benefits from simpler strategies, because of the homozygosity status of the starting material. In turn, engineered products are expected to attain broader immune compatibility than natural counterparts. Several international initiatives focus on iPSC development from haplo-selected individuals, including programs in Japan 32 , Australia 31 , South Korea 33 , 34 , Spain 35 , Germany 36 , Lithuania, Saudi Arabia 37 . We modeled the impact of “ HLA-C retained ” gene-editing intervention on the 51 naturally occurring super donors and found that their immune compatibility could be enhanced to match 97.9% of the Lithuanian population, 95.7% of the European-American population and 95.5% of the British population. Conversely, the immune compatibility provided by the HLA-A and HLA-B double-homozygous was limited due to the retained diversity within the heterozygous HLA-C allele. Here, we propose the term “ synthetic superdonor” for those cell lines derived from naturally occurring superdonors that, through means of gene editing, acquire broader immune compatibility. Analysis on the gene editing availability for HLA-A , HLA-B and HLA-C highlights the importance of protein topology, knock-out strategy design and nuclease target site activity to achieve synthetic superdonor stocks. The HLA-A, HLA-B and HLA-C proteins are of the type-I transmembrane class; hence, targeting the N-terminus ectodomain slightly constrains the number of available Cas9 binding sites. Our analyses demonstrate that the largest impact to knock-out availability is the nuclease activity score; therefore, gene editing tools that enhance nuclease activity are likely to have a positive impact on synthetic superdonor creation in the future. Likewise, our analyses indicate that naturally occurring superdonor and synthetic superdonor cell sources would positively impact the immune matching for rare haplotypes. Both, naturally occurring and synthetic superdonors offer are a remarkable source for regenerative medicine applications and for the creation of derivative advanced therapeutic medicinal products (ATMPs). Disclosures The authors of this manuscript have no conflicts of interest to disclose. Data availability statement All data is made available through public repositories. Acknowledgements This project was supported by Mission-driven Implementation of Science and Innovation Program 02-002-P-0001 to D.B. and J.A., funded by the Economic Revitalization and Resilience Enhancement Plan “New Generation Lithuania”. All authors read and agreed to the last version of the manuscript. Footnotes ↵ 11 co-first author References 1. ↵ Cao K , Hollenbach J , Shi X , Shi W , Chopek M , Fernández-Viña MA . Analysis of the frequencies of HLA-A, B, and C alleles and haplotypes in the five major ethnic groups of the United States reveals high levels of diversity in these loci and contrasting distribution patterns in these populations . Human Immunology . 2001 ; 62 ( 9 ): 1009 – 1030 . doi: 10.1016/S0198-8859(01)00298-1 OpenUrl CrossRef PubMed Web of Science 2. ↵ Mack SJ , Tu B , Lazaro A , et al. HLA-A, -B, -C, and -DRB1 allele and haplotype frequencies distinguish Eastern European Americans from the general European American population . Tissue Antigens . 2009 ; 73 ( 1 ): 17 – 32 . doi: 10.1111/j.1399-0039.2008.01151.x OpenUrl CrossRef PubMed 3. ↵ Barker DJ , Maccari G , Georgiou X , et al. The IPD-IMGT/HLA Database . Nucleic Acids Research . 2023 ; 51 ( D1 ): D1053 – D1060 . doi: 10.1093/nar/gkac1011 OpenUrl CrossRef PubMed 4. ↵ Jamy O , Zeiser R , Chen YB . Novel developments in the prophylaxis and treatment of acute GVHD . Blood . 2023 ; 142 ( 12 ): 1037 – 1046 . doi: 10.1182/blood.2023020073 OpenUrl CrossRef PubMed 5. Holtzman NG , Curtis LM , Salit RB , et al. High-dose alemtuzumab and cyclosporine vs tacrolimus, methotrexate, and sirolimus for chronic graft-versus-host disease prevention . Blood Advances . 2024 ; 8 ( 16 ): 4294 – 4310 . doi: 10.1182/bloodadvances.2023010973 OpenUrl CrossRef PubMed 6. ↵ Hricik DE , Armstrong B , Alhamad T , et al. Infliximab Induction Lacks Efficacy and Increases BK Virus Infection in Deceased Donor Kidney Transplant Recipients: Results of the CTOT-19 Trial . JASN . 2023 ; 34 ( 1 ): 145 – 159 . doi: 10.1681/ASN.2022040454 OpenUrl CrossRef PubMed 7. ↵ Bacigalupo A , Metafuni E , Amato V , Marquez Algaba E , Pagano L . Reducing infectious complications after allogeneic stem cell transplant . Expert Review of Hematology . 2020 ; 13 ( 11 ): 1235 – 1251 . doi: 10.1080/17474086.2020.1831382 OpenUrl CrossRef PubMed 8. ↵ Styczynski J , Tridello G , Koster L , et al. Decrease of lethal infectious complications in the context of causes of death (COD) after hematopoietic cell transplantation: COD-2 and COD-1 study of the Infectious Diseases Working Party EBMT . Bone Marrow Transplant . 2023 ; 58 ( 8 ): 881 – 892 . doi: 10.1038/s41409-023-01998-2 OpenUrl CrossRef PubMed 9. ↵ Lee SJ , Klein J , Haagenson M , et al. High-resolution donor-recipient HLA matching contributes to the success of unrelated donor marrow transplantation . Blood . 2007 ; 110 ( 13 ): 4576 – 4583 . doi: 10.1182/blood-2007-06-097386 OpenUrl Abstract / FREE Full Text 10. ↵ Spellman SR. Hematology 2022—what is complete HLA match in 2022? Hematology . 2022 ; 2022 ( 1 ): 83 – 89 . doi: 10.1182/hematology.2022000326 OpenUrl CrossRef PubMed 11. ↵ Kitano Y , Nishimura S , Kato TM , et al. Generation of hypoimmunogenic induced pluripotent stem cells by CRISPR-Cas9 system and detailed evaluation for clinical application . Molecular Therapy - Methods & Clinical Development . 2022 ; 26 : 15 – 25 . doi: 10.1016/j.omtm.2022.05.010 OpenUrl CrossRef PubMed 12. ↵ Gaykema LH , Van Nieuwland RY , Lievers E , et al. T-Cell Mediated Immune Rejection of Beta-2-Microglobulin Knockout Induced Pluripotent Stem Cell-Derived Kidney Organoids . Stem Cells Translational Medicine . 2024 ; 13 ( 1 ): 69 – 82 . doi: 10.1093/stcltm/szad069 OpenUrl CrossRef PubMed 13. ↵ Gornalusse GG , Hirata RK , Funk SE , et al. HLA-E-expressing pluripotent stem cells escape allogeneic responses and lysis by NK cells . Nat Biotechnol . 2017 ; 35 ( 8 ): 765 – 772 . doi: 10.1038/nbt.3860 OpenUrl CrossRef PubMed 14. ↵ Deuse T , Hu X , Gravina A , et al. Hypoimmunogenic derivatives of induced pluripotent stem cells evade immune rejection in fully immunocompetent allogeneic recipients . Nat Biotechnol . 2019 ; 37 ( 3 ): 252 – 258 . doi: 10.1038/s41587-019-0016-3 OpenUrl CrossRef PubMed 15. ↵ Pizzato HA , Alonso-Guallart P , Woods J , et al. Engineering human pluripotent stem cell lines to evade xenogeneic transplantation barriers . Stem Cell Reports . 2024 ; 19 ( 2 ): 299 – 313 . doi: 10.1016/j.stemcr.2023.12.003 OpenUrl CrossRef PubMed 16. ↵ Han X , Wang M , Duan S , et al. Generation of hypoimmunogenic human pluripotent stem cells . Proc Natl Acad Sci USA . 2019 ; 116 ( 21 ): 10441 – 10446 . doi: 10.1073/pnas.1902566116 OpenUrl Abstract / FREE Full Text 17. ↵ Xu H , Wang B , Ono M , et al. Targeted Disruption of HLA Genes via CRISPR-Cas9 Generates iPSCs with Enhanced Immune Compatibility . Cell Stem Cell . 2019 ; 24 ( 4 ): 566 – 578 .e7. doi: 10.1016/j.stem.2019.02.005 OpenUrl CrossRef PubMed 18. ↵ Urnikyte A , Flores-Bello A , Mondal M , et al. Patterns of genetic structure and adaptive positive selection in the Lithuanian population from high-density SNP data . Sci Rep . 2019 ; 9 ( 1 ): 9163 . doi: 10.1038/s41598-019-45746-3 OpenUrl CrossRef PubMed 19. ↵ Creary LE , Gangavarapu S , Mallempati KC , et al. Next-generation sequencing reveals new information about HLA allele and haplotype diversity in a large European American population . Human Immunology . 2019 ; 80 ( 10 ): 807 – 822 . doi: 10.1016/j.humimm.2019.07.275 OpenUrl CrossRef PubMed 20. ↵ Davey S , Ord J , Navarrete C , Brown C. HLA-A, -B and -C allele and haplotype frequencies defined by next generation sequencing in a population of 519 English blood donors . Human Immunology . 2017 ; 78 ( 5-6 ): 397 – 398 . doi: 10.1016/j.humimm.2017.04.001 OpenUrl CrossRef PubMed 21. ↵ Tu B , Mack SJ , Lazaro A , et al. HLA-A, -B, -C, -DRB1 allele and haplotype frequencies in an African American population . Tissue Antigens . 2007 ; 69 ( 1 ): 73 – 85 . doi: 10.1111/j.1399-0039.2006.00728.x OpenUrl CrossRef PubMed 22. ↵ Kuhn M . Building Predictive Models in R Using the caret Package . J Stat Soft . 2008 ; 28 ( 5 ). doi: 10.18637/jss.v028.i05 OpenUrl CrossRef PubMed 23. ↵ Robinson J , Barker DJ , Marsh SGE . 25 years of the IPD-IMGT/HLA D atabase . HLA . 2024 ; 103 ( 6 ): e15549 . doi: 10.1111/tan.15549 OpenUrl CrossRef 24. ↵ Hoberecht L , Perampalam P , Lun A , Fortin JP . A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies . Nat Commun . 2022 ; 13 ( 1 ): 6568 . doi: 10.1038/s41467-022-34320-7 OpenUrl CrossRef PubMed 25. ↵ Hallgren J , Tsirigos KD , Pedersen MD , et al. DeepTMHMM Predicts Alpha and Beta Transmembrane Proteins Using Deep Neural Networks . Bioinformatics ; 2022 . doi: 10.1101/2022.04.08.487609 OpenUrl CrossRef 26. ↵ Doench JG , Fusi N , Sullender M , et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9 . Nat Biotechnol . 2016 ; 34 ( 2 ): 184 – 191 . doi: 10.1038/nbt.3437 OpenUrl CrossRef PubMed 27. ↵ Stockton JD , Nieto T , Wroe E , et al. Rapid, highly accurate and cost-effective open-source simultaneous complete HLA typing and phasing of class I and II alleles using nanopore sequencing . HLA . 2020 ; 96 ( 2 ): 163 – 178 . doi: 10.1111/tan.13926 OpenUrl CrossRef PubMed 28. ↵ Pollock NR , Farias TDJ , Kichula KM , et al. The 18th International HLA & Immunogenetics workshop project report: Creating fully representative MHC reference haplotypes . HLA . 2024 ; 103 ( 6 ): e15568 . doi: 10.1111/tan.15568 OpenUrl CrossRef PubMed 29. ↵ Sullivan S , Ginty P , McMahon S , et al. The Global Alliance for iPSC Therapies (GAiT) . Stem Cell Research . 2020 ; 49 : 102036 . doi: 10.1016/j.scr.2020.102036 OpenUrl CrossRef PubMed 30. ↵ Taylor CJ , Peacock S , Chaudhry AN , Bradley JA , Bolton EM . Generating an iPSC Bank for HLA-Matched Tissue Transplantation Based on Known Donor and Recipient HLA Types . Cell Stem Cell . 2012 ; 11 ( 2 ): 147 – 152 . doi: 10.1016/j.stem.2012.07.014 OpenUrl CrossRef PubMed Web of Science 31. ↵ Tian P , Elefanty A , Stanley EG , Durnall JC , Thompson LH , Elwood NJ . Creation of GMP-Compliant iPSCs From Banked Umbilical Cord Blood . Front Cell Dev Biol . 2022 ; 10 : 835321 . doi: 10.3389/fcell.2022.835321 OpenUrl CrossRef 32. ↵ Yoshida S , Kato TM , Sato Y , et al. A clinical-grade HLA haplobank of human induced pluripotent stem cells matching approximately 40% of the Japanese population . Med . 2023 ; 4 ( 1 ): 51 – 66 .e10. doi: 10.1016/j.medj.2022.10.003 OpenUrl CrossRef 33. ↵ Rim YA , Park N , Nam Y , et al. Recent progress of national banking project on homozygous HLA - typed induced pluripotent stem cells in S outh K orea . J Tissue Eng Regen Med . 2018 ; 12 ( 3 ). doi: 10.1002/term.2578 OpenUrl CrossRef 34. ↵ Lee S , Huh JY , Turner DM , et al. Repurposing the Cord Blood Bank for Haplobanking of HLA-Homozygous iPSCs and Their Usefulness to Multiple Populations . Stem Cells . 2018 ; 36 ( 10 ): 1552 – 1566 . doi: 10.1002/stem.2865 OpenUrl CrossRef PubMed 35. ↵ Kuebler B , Alvarez-Palomo B , Aran B , et al. Generation of a bank of clinical-grade, HLA-homozygous iPSC lines with high coverage of the Spanish population . Stem Cell Res Ther . 2023 ; 14 ( 1 ): 366 . doi: 10.1186/s13287-023-03576-1 OpenUrl CrossRef PubMed 36. ↵ Liedtke S , Korschgen L , Korn J , Duppers A , Kogler G . GMP-grade CD34 + selection from HLA-homozygous licensed cord blood units and short-term expansion under European ATMP regulations . Vox Sanguinis . 2021 ; 116 ( 1 ): 123 – 135 . doi: 10.1111/vox.12978 OpenUrl CrossRef PubMed 37. ↵ Alowaysi M , Lehmann R , Al-Shehri M , et al. HLA-based banking of induced pluripotent stem cells in Saudi Arabia . Stem Cell Res Ther . 2023 ; 14 ( 1 ): 374 . doi: 10.1186/s13287-023-03612-0 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted May 22, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Identification of HLA-A, HLA-B and HLA-C triple homozygous and double homozygous donors: a path towards synthetic superdonor Advanced Therapeutic Medicinal Products Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Identification of HLA-A , HLA-B and HLA-C triple homozygous and double homozygous donors: a path towards synthetic superdonor Advanced Therapeutic Medicinal Products Daniel Naumovas , Barbara Rojas-Araya , Catalina M. Polanco , Victor Andrade , Rita Čekauskienė , Beatričė Valatkaitė-Rakštienė , Inga Laurinaitytė , Artūras Jakubauskas , Mindaugas Stoškus , Laimonas Griškevičius , Ivan Nalvarte , Jose Inzunza , Daiva Baltriukienė , Jonathan Arias medRxiv 2025.05.19.25327154; doi: https://doi.org/10.1101/2025.05.19.25327154 Share This Article: Copy Citation Tools Identification of HLA-A , HLA-B and HLA-C triple homozygous and double homozygous donors: a path towards synthetic superdonor Advanced Therapeutic Medicinal Products Daniel Naumovas , Barbara Rojas-Araya , Catalina M. Polanco , Victor Andrade , Rita Čekauskienė , Beatričė Valatkaitė-Rakštienė , Inga Laurinaitytė , Artūras Jakubauskas , Mindaugas Stoškus , Laimonas Griškevičius , Ivan Nalvarte , Jose Inzunza , Daiva Baltriukienė , Jonathan Arias medRxiv 2025.05.19.25327154; doi: https://doi.org/10.1101/2025.05.19.25327154 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (570) Allergy and Immunology (864) Anesthesia (302) Cardiovascular Medicine (4445) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1515) Epidemiology (15236) Forensic Medicine (30) Gastroenterology (1127) Genetic and Genomic Medicine (6610) Geriatric Medicine (669) Health Economics (1000) Health Informatics (4549) Health Policy (1370) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1266) Infectious Diseases (except HIV/AIDS) (15926) Intensive Care and Critical Care Medicine (1104) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6613) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1147) Occupational and Environmental Health (957) Oncology (3341) Ophthalmology (975) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1694) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5458) Public and Global Health (9244) Radiology and Imaging (2205) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1197) Rheumatology (596) Sexual and Reproductive Health (715) Sports Medicine (530) Surgery (713) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a025af6a5b40aa64',t:'MTc3OTg5MTg4Nw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();