Abstract
Background:
Xenotransfusion using genetically engineered (GE) pig red blood cells (RBCs) offers a promising solution to blood shortages, particularly in emergency settings. Although nonhuman primates (NHPs) have been widely used in preclinical studies, their translational relevance is limited by species-specific immune responses and logistical challenges. This study aimed to evaluate whether brain-dead humans could serve as a human translational reference model by characterizing and comparing their hematologic, biochemical, and immunologic profiles with those of patients with acute blood loss (ABL). The goal was to generate baseline data to inform the design and interpretation of future in vivo studies involving the transfusion of GE pig RBCs.
Materials and methods:
Comprehensive clinical and immunological analyses were performed on donation after brain death (DBD) subjects (n=179) and patients with ABL requiring transfusion (n=104). The parameters included hematological indices, electrolytes, coagulation factors, inflammatory biomarkers, and arterial blood gases. Immune assays were conducted on sera from DBD subjects (n=31) and patients with ABL (n=102) to examine IgM/IgG binding and complement-dependent cytotoxicity (CDC) against triple-knockout (TKO) pig RBCs lacking Gal, Neu5Gc, and Sda antigens.
Results:
Across most measured parameters, overlapping ranges in hematologic and biochemical indices were observed between DBD subjects and patients with ABL. Anti-TKO IgM/IgG binding and CDC were not detectably different between the two groups under the conditions tested. However, differences were observed in several other immune parameters, including hemagglutination, cytokine profiles, total immunoglobulin levels, and complement components.
Conclusion:
Brain-dead humans may represent an ethically feasible human translational reference model for xenotransfusion research. While DBD subjects do not fully reproduce the physiologic and inflammatory milieu of patients with ABL, they provide useful baseline human data for assessing selected early xenoreactive responses to GE pig RBCs and may help bridge the translational gap between NHP studies and future clinical application.
Introduction
The global shortage of human red blood cells (RBCs) poses a critical challenge in transfusion medicine (; ; ), particularly in acute blood loss (ABL) scenarios such as trauma, surgery, and postpartum hemorrhage. This issue is further compounded for patients with rare blood types (; ), individuals sensitized to human RBCs (; ; ; ; ; ; ; ; ; ), or those in regions with high prevalence of bloodborne pathogens such as human immunodeficiency virus, hepatitis viruses, or malaria (; ). Alternative and sustainable sources of RBCs are urgently needed to ensure adequate supply and compatibility. One promising solution is xenotransfusion using genetically engineered (GE) pig RBCs (; ; ; ; ; ; ; ).
Recent advances in gene-editing technologies have enabled the production of pig RBCs lacking major xenoantigens (; ; ; ; ; ), such as galactose-α1,3-galactose (Gal), N-glycolylneuraminic acid (Neu5Gc), and Sda, and incorporated with human transgenes, including CD55 to reduce immune reactivity (). These triple-knockout (TKO) modifications significantly minimize human antibody binding and complement-dependent cytotoxicity (CDC) compared with wild-type (WT) or α1,3-galactosyltransferase-knockout (GTKO) pigs (; ; ; ; ).
Despite these advances, the preclinical evaluation of GE pig RBCs in vivo remains limited largely because of the difficulty in establishing reliable animal models that can accurately reflect human immune responses and support reproducible measurement of RBC survival. Early studies using baboons demonstrated that unmodified pig RBCs were rapidly cleared within 5 minutes (). However, the enzymatic removal of αGal antigens with α-galactosidase significantly extended RBC survival to approximately 2 hours. Combining this procedure with complement depletion using cobra venom factor increased the survival to 24 hours. Additional interventions, including the co-administration of bovine serum albumin–Gal conjugates and phagocytosis inhibitors such as medronate liposomes, further prolonged survival beyond 72 hours. Transfusion of large volumes also achieved modest prolongation of RBC survival, though the effect was associated with adverse effects such as splenic congestion and follicular hyperplasia ().
Approaches to camouflaging non-Gal antigens using succinimid propionate-linked methoxypolyethyleneglycol have been explored. This strategy extended RBC survival in rhesus monkeys up to 12 hours without immunosuppression when combined with α-galactosidase treatment and up to 40 hours when immunosuppressive therapy was added ().
Another strategy involved expressing human complement-regulatory and antiphagocytic proteins, such as CD55 and CD47, on pig RBCs (). However, in vivo studies showed that TKO/CD55/CD47 pig RBCs transfused into cynomolgus monkeys survived less than 2 hours, which was only a modest improvement over WT controls (). Promising results were obtained when TKO pig RBCs were transfused into New World (NW) nonhuman primates (NHPs), specifically capuchin monkeys, without immunosuppression. In this setting, RBC survival reached 5–7 days (). The low levels of anti-TKO IgM antibodies in the recipient monkeys did not hinder their short-term survival. This finding suggests that extended survival is achievable in recipients with minimal or absent anti-TKO antibodies, particularly when combined with further pharmacologic modulation of complement or phagocytosis pathways.
The above incremental advances underscore the limitations of NHP models for xenotransfusion. All NHP species tested to date, especially Old World (OW) NHPs such as baboons and rhesus or cynomolgus monkeys, are crossmatch-positive to TKO pig RBCs due to species-specific immunologic backgrounds. This intrinsic incompatibility limits their utility for evaluating the safety and efficacy of GE pig RBCs in a clinically relevant setting.
Brain-dead humans have recently emerged as a novel preclinical model to address the abovementioned research gap. These subjects provide an ethically feasible and clinically relevant platform for evaluating immune responses to xenogeneic cells under controlled conditions (; ; ; ; ; ; ; ; ). Although their use has been successfully demonstrated in solid organ xenotransplantation studies (), their potential as a model for xenotransfusion remains underexplored. In particular, the resemblance of their pathophysiological and immunological characteristics to those of patients with ABL, which is a critical consideration for validating their use as reference models, remains unclear.
This study aimed to evaluate the suitability of brain-dead humans as a human translational reference model for xenotransfusion. We compared hematological, biochemical, and immunological characteristics between brain-dead humans and patients with ABL using standardized protocols within a single institutional and laboratory framework. The resulting dataset provides a foundational human reference for the design and interpretation of future in vivo studies involving GE pig RBCs.
Methods
Study design and ethical approval
This study was conducted under the approval of the Institutional Review Board (IRB) of the Second Affiliated Hospital of Hainan Medical University (IRB- LW2022901 and IRB- LW2022231). Serum, plasma, and blood samples were collected from donation after brain death (DBD) subjects through an organ procurement organization in Hainan Province and from patients with ABL through the Department of Emergency at the Second Affiliated Hospital. Healthy adult volunteers provided informed consent for their participation. For the DBDs, written informed consent was obtained from their next of kin at the time of organ donation. The consent process included explicit approval for the collection and use of biological samples such as blood (including serum and plasma) and tissues in the clinical evaluation of organ function and safety, and future research purposes. All procedures complied with applicable institutional, national, and international ethical guidelines, including IRB oversight.
Demographic and clinical characteristics of participants
A total of 179 DBDs and 104 patients with ABL were included in this study. Their demographic and clinical characteristics, including age, gender, and cause of brain death or ABL, are summarized in Table 1.
Table 1
| Characteristic | Brain-dead donors (DBD) | Acute blood loss (ABL) patients | P value |
|---|---|---|---|
| Total number | 179 | 104 | – |
| Age, mean (± SD), years | 46.4 (± 25.3) | 47.3 (± 14.0) | 0.43 |
| Gender | |||
| - Male | 151 (84%) | 74 (71.2%) | 0.34 |
| - Female | 28 (16%) | 30 (28.8%) | 0.58 |
| Cause of condition | |||
| - Spontaneous cerebral hemorrhage | 103 (58%) | – | – |
| - Traumatic cerebral hemorrhage | 58 (32%) | – | – |
| - Cerebral infarction | 5 (3%) | – | – |
| - Organ injury | – | 31 (30%) | – |
| - Bone fracture | – | 42 (40%) | – |
| - Craniocerebral injury | – | 19 (18%) | – |
| - Other | 13 (7%) | 12 (12%) | – |
| Injury details (ABL) | |||
| - Traffic | – | 90 (87%) | – |
| - Fallen | – | 14 (13%) | – |
Demographic and clinical characteristics of research subjects.
The patients with ABL were categorized into four groups according to their injury severity score (ISS) () for the subsequent analysis of immune responses to TKO pig RBCs (see Results for details).
Sample collection and processing
Blood samples were analyzed as whole blood or processed into serum and plasma fractions. Serum samples from 37 DBD subjects and 102 patients with ABL were available for immunological analyses; however, the number of samples differed by assay because of sample availability and volume constraints, as shown in Table 2. In particular, anti-TKO pig RBC response assays were performed in a subset of 31 DBD samples. All the samples were processed and stored under standardized conditions to maintain their integrity.
Table 2
| Mean (± SD), Number | DBD | ABL patients | P value |
|---|---|---|---|
| Hematological parameters | |||
| RBC (1×106/μL) | 3.61 (0.98), n=179 | 3.42 (0.8), n=104 | 0.222 |
| Hb (mg/mL) | 107.9 (27.66), n=179 | 99.9 (20.6), n=104 | 0.0357 |
| HCT (%) | 32 (8), n=179 | 30 (6), n=104 | 0.12 |
| WBC (1×106/μL) | 12.82 (6.35), n=179 | 13.29 (5.17), n=104 | 0.5824 |
| Platelet (1×103/μL) | 185.3 (116.10), n=179 | 185.7 (75.79), n=104 | 0.323 |
| Neutrophil number (1×106/μL) | 11.23 (5.42), n=179 | 11.62 (5.22), n=104 | 0.3146 |
| Lymphocyte number (1×106/μL) | 1.08 (0.69), n=179 | 1.09 (0.56), n=104 | 0.5954 |
| Monocyte number (1×106/μL) | 0.82 (0.94), n=179 | 0.73 (0.35), n=104 | 0.2381 |
| Neutrophil (%) | 83.73 (7.30), n=179 | 84.81 (6.79), n=104 | 0.1673 |
| Lymphocytes (%) | 9.80 (7.31), n=179 | 9.08 (5.83), n=104 | 0.3577 |
| Monocytes (%) | 5.64 (3.31), n=179 | 5.63 (2.19), n=104 | 0.4267 |
| Biochemical parameters | |||
| AST (U/L) | 89.62 (157.01), n=179 | 118.3 (239.5), n=101 | 0.9914 |
| ALT (U/L) | 72.95 (115.01), n=179 | 73.47 (143.0), n=101 | 0.1791 |
| Total bilirubin (TB, mg/dL) | 1.31 (1.35), n=179 | 0.94 (0.76), n=101 | 0.0101 |
| Creatinine (Cr, mg/dL) | 1.04 (0.69), n=179 | 0.78 (0.72), n=101 | <0.0001 |
| Creatine kinase-muscle/brain (CK-MB, (U/L) | 52.88 (59.06), n=160 | 65.06 (81.28), n=55 | 0.2168 |
| Myoglobin (Mb, ng/mL) | 545.90 (450.2), n=104 | 679.5 (464.3), n=45 | 0.0683 |
| Albumin (g/L) | 38.25 (8.10), n=179 | 33.3 (7.3), n=101 | <0.0001 |
| Globulin (g/L) | 23.59 (6.27), n=179 | 17.76 (5.44), n=101 | <0.0001 |
| Albumin/globulin | 1.76 (0.68), n=179 | 1.99 (0.54), n=101 | <0.0001 |
| LDH (U/L) | 444 (500.60), n=125 | 448 (415), n=87 | 0.3226 |
| Electrolytes | |||
| Sodium (Na, mmol/L) | 147.90 (10.79), n=179 | 142.2 (4.39), n=104 | <0.0001 |
| Chlorine (Cl, mmol/L) | 111.20 (10.18), n=179 | 106.8 (6.33), n=104 | 0.0017 |
| Potassium (K, mmol/L) | 4.04 (0.86), n=179 | 3.99 (0.61), n=104 | 0.5829 |
| Calcium (Ca, mmol/L) | 2.24 (0.27), n=172 | 1.74 (0.45), n=81 | <0.0001 |
| Magnesium (Mg, mmol/L) | 0.90 (0.16), n=146 | 0.69 (0.10), n=11 | <0.0001 |
| Phosphorus (P, mmol/L) | 0.95 (0.68), n=127 | 1.17 (0.82), n=47 | 0.0243 |
| Coagulation and inflammatory markers | |||
| Antithrombin III (AT-III, %) | 65.90 (32.30), n=152 | 65.78 (24.88), n=11 | 0.8096 |
| D-dimer (mg/L) | 14.19 (25.75), n=145 | 25.84 (31.77), n=74 | <0.0001 |
| Fibrinogen (FIB, g/L) | 4.85 (2.44), n=174 | 2.76 (1.97), n=100 | <0.0001 |
| C-reactive protein (CRP, mg/L) | 127.8 (99.70), n=178 | 42.6 (59.7), n=99 | <0.0001 |
| Arterial blood gas test | |||
| PH | 7.39 (0.12), n=172 | 7.36 (0.09), n=75 | 0.0017 |
| HCO3− (mmol/L) | 23.96 (4.71), n=176 | 22.59 (4.22), n=76 | 0.046 |
| PaO2 (mmHg) | 114.20 (62.57), n=176 | 139.7 (57.31), n=75 | 0.0062 |
| PaCO2 (mmHg) | 47.19 (17.08), n=179 | 37.32 (10.75), n=75 | <0.0001 |
| Lactic acid (mmol/L) | 2.36 (2.18), n=173 | 3.2 (2.9), n=60 | 0.0097 |
| Immunological parameters | |||
| Total IgM (g/L) | 1.38 (1.08), n=45 | 0.65 (0.21), n=13 | 0.026 |
| Total IgG (g/L) | 9.04 (3.41), n=45 | 6.21 (3.26), n=13 | 0.0193 |
| Total IgA (g/L) | 3.03 (1.81), n=45 | 1.26 (0.51), n=13 | <0.0001 |
| C3 (g/L) | 1.02 (0.38), n=45 | 0.65 (0.32), n=13 | 0.0005 |
| C4 (g/L) | 0.30 (1.14), n=45 | 0.13 (0.05), n=13 | <0.0001 |
| IL-2 (pg/mL) | 1.36 (1.30), n=37 | 0.8 (0.95), n=101 | <0.0001 |
| IL-4 (pg/mL) | 3.08 (2.04), n=37 | 9.27 (9.56), n=101 | <0.0001 |
| IL-6 (pg/mL) | 599.90 (840.20), n=37 | 878 (2684), n=101 | 0.9457 |
| IL-10 (pg/mL) | 20.49 (62.81), n=37 | 99.09 (167.3), n=101 | <0.0001 |
| IFN-γ (pg/mL) | 0.96 (2.10), n=37 | 31.16 (24.8), n=101 | <0.0001 |
| TNF-α (pg/mL) | 1.58 (0), n=37 | 25.33 (21.02), n=101 | <0.0001 |
| Anti-TKO pig RBC response: | |||
| IgM binding (rGM) | 3.61 (4.84), n=31 | 3.31 (2.98), n=102 | 0.6142 |
| IgG binding (rGM) | 14.59 (51.40), n=31 | 1.48 (0.43), n=102 | 0.1483 |
| CDC (cytotoxicity%) | 13.43 (6.21), n=31 | 16.67 (11.44), n=102 | 0.9206 |
| Hemagglutination | 1.55 (1.15), n=31 | 2.85 (1.56), n=102 | <0.0001 |
Comparison of hematological, biochemical, electrolyte, coagulation, inflammatory, and immunological parameters between brain-dead donors (DBD) and acute blood loss (ABL) patients.
rGM, relative geometric mean; DBD, brain-dead donor; ABL, acute blood loss.
Laboratory analysis
Comprehensive evaluations were conducted to analyze hematological, biochemical, electrolyte, coagulation, inflammatory marker, and arterial blood gas parameters (Table 2). All routine analyses were performed at the Central Laboratory of the Second Affiliated Hospital of Hainan Medical University using automated and standardized systems. Complement activity was evaluated using the Zybio platform (Zybio, Chongqing, China), and cytokine profiles were quantified using the Agilent system (Agilent, USA, SK00024AAJ). Flow cytometric analyses were performed using the NoVoCyte D3000 flow cytometer (Agilent Technologies, Beijing, China).
In vitro assessments of anti-triple-knockout (TKO, lacking Gal/Neu5Gc/Sda expression) pig RBC responses were also conducted (Table 2). These assays included IgM and IgG antibody binding, complement-dependent cytotoxicity (CDC), and hemagglutination as described in detail in the In vitro assays section below.
Preparation of RBCs
Blood from TKO pigs (blood type O [non-A]) (; ; ) was provided by Chengdu Clonorgan Biotechnology Co., Ltd. under Institutional Animal Care and Use Committee approval (IACUC#ZK09-24-01A). RBCs were isolated from the heparinized blood through three wash cycles with 1× phosphate-buffered saline (PBS; Gibco, Shanghai, China) at 700g for 5 min at 4 °C (; ).
The expression of Gal, Neu5Gc, and Sda on pig RBCs was evaluated by CytoFLEX flow cytometry (Beckman Coulter, Brea, CA, USA) using the following antibodies: FITC-conjugated BSI-B4 lectin (Sigma, L2895, Shanghai, China) for Gal, FITC-conjugated Dolichos biflorus agglutinin (Vector Laboratories, FL-1031, Shanghai, China) for Sda, and a primary chicken anti-Neu5Gc antibody (BioLegend, #146901, Beijing, China) with an Alexa Fluor488 goat anti-chicken secondary antibody (Abcam, ab96947, Shanghai, China) (Li et al., 2021; Li et al., 2022). A chicken IgY isotype control (BioLegend, #402101) was employed as the negative control. The TKO RBCs were confirmed to lack Gal, Neu5Gc, and Sda expression. Meanwhile, wild-type pig RBCs and human RBCs (blood type O) served as the positive and negative controls, respectively (data not shown).
Detection of CD45 and SLA class I antigens
The surface expression of CD45 (mouse anti-pig CD45 antibody [FITC, Clone: K252.1E4, Bio-Rad, MCA1222A647, USA]) and SLA class I antigen (mouse anti-pig SLA class I antibody [FITC or Alexa Fluor® 647, Clone: JM1E3, Bio-Rad, MCA2261A647, USA]) on pig RBCs was analyzed by CytoFLEX flow cytometry. The purity of the isolated RBCs was confirmed to exceed 99% (data not shown). Platelet depletion was not separately quantified.
IgM and IgG antibody binding to TKO pig RBCs
Serum samples were decomplemented by heat inactivation at 56 °C for 30 min and stored at −80 °C until further use. The binding of IgM and IgG antibodies to TKO pig RBCs was assessed following established protocols (Li et al., 2022).
TKO pig RBCs or human RBCs (blood group O, negative control; 1×106 cells/150 µL of PBS) were incubated with 50 µL of serum for 30 min at 4 °C. After incubation, the RBCs were washed and suspended in 100 µL of PBS containing 10% goat serum for blocking (20 min at 4 °C). Afterward, the RBCs were incubated with Alexa Fluor® 647 AffiniPure™ goat anti-human IgM, Fc5μ fragment specific, and Alexa Fluor® 488 AffiniPure™ goat anti-human IgG (H+L; Jackson ImmunoResearch, USA; 1:1000) antibodies for 30 min in the dark at 4 °C. Following antibody labeling, the RBCs were washed and resuspended in 200 µL of PBS. Flow cytometry was performed to measure antibody binding. Data were analyzed with FlowJo V10 (Tree Star, Ashland, OR, USA). Antibody binding was expressed as a relative geometric mean, calculated by dividing the geometric mean of each sample by that of the negative control (secondary antibody without serum) (Li et al., 2019; Li et al., 2020; Li et al., 2021; Li et al., 2022; Oscherwitz et al., 2022). Human O RBCs served as the negative control.
Serum CDC assay: hemolytic assay
The CDC of serum (at a final concentration of 25%) against TKO pig RBCs was assessed by hemolytic assay (). In brief, 100 µL of 50% heat-inactivated serum, 100 µL of PBS buffer (blank/serum-free control), or 0.1% Triton X-100 buffer (positive control; Sigma) was incubated with 100 µL of pig RBCs (5×107 cells/mL) at 4 °C for 30 min, resulting in a final serum concentration of 25%. Following incubation, the mixture was washed with PBS and centrifuged at 500g for 5 min. The supernatant was carefully aspirated, and 400 µL of 30% rabbit complement (Cedarlane, CL3441, Canada) or 400 µL of PBS (blank control) was added to the RBC pellet. The mixture was incubated at 37 °C for 30 min. After incubation, the samples were centrifuged at 500g for 5 min, and 100 µL of the supernatant was carefully collected in triplicate (a total of 300 µL) to avoid disturbing the RBC pellet. The supernatant was transferred to UV-transparent 96-well microplates (Corning, #3635, Shanghai, China), and absorbance was measured at 560 nm using a spectrophotometer (Thermo Fisher Scientific, Multiskan™ FC, Shanghai, China). Each sample was analyzed in triplicate.
CDC (%) was calculated using the following formula:
Hemagglutination assay
Isolated TKO pig RBCs were reconstituted with PBS containing Ca2+/Mg2+ and transferred to each well (20 μL of RBCs at a concentration of 5×108/mL) of a 96-well flat plate (Corning), followed by the addition of 20 μL of 50% heat-inactivated serum (final serum concentration of 25%). The mixture was incubated at room temperature for 60 min. Observations were performed under a microscope (Nikon, TS2-FL, Japan), and images were captured using NIS-Elements software. Agglutination was examined using the modified Marsh scoring method (Marsh, 1972; ).
To serve as negative controls, human blood type O red blood cells were included in all IgM/IgG binding, hemagglutination, and CDC assays.
Statistical analysis
The normality of data distribution was evaluated using the Shapiro–Wilk test in GraphPad Prism 8 (GraphPad Software, La Jolla, CA, USA). On the basis of the results, nonparametric tests were used for group comparisons when the data did not meet the assumptions of normality. The Mann–Whitney U test was used for comparisons between two groups, and the Kruskal–Wallis test followed by Dunn’s post hoc test was applied for comparisons among three or more groups. All statistical analyses were performed using GraphPad Prism 8. Data were presented as mean ± standard deviation (SD), unless otherwise indicated. A p value of <0.05 was considered statistically significant. Statistical significance was annotated as follows: p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), and p < 0.0001 (****).
Declaration of generative AI and AI-assisted technologies in the writing process
During the early stages of manuscript preparation, the authors used ChatGPT (OpenAI, GPT-4) to assist with language refinement and improving clarity. However, the final version of the manuscript was professionally edited by a language editing company that does not use AI tools. No content generation, data interpretation, or scientific reasoning was delegated to any AI. All scientific content and conclusions were conceived, written, and verified by the authors. This declaration is made in accordance with the TITAN Guidelines 2025 on the use of artificial intelligence in scientific publishing (Agha et al., 2025).
Results
Hematological parameters
No significant differences in RBC count or hematocrit (HCT) were observed between the DBDs and patients with ABL (Figure 1A). However, hemoglobin (Hb) levels were significantly lower in the patients with ABL than in the DBDs (p < 0.05). Similarly, no significant differences in white blood cell (WBC), neutrophil, lymphocyte, platelet, and monocyte counts and their percentages existed between the two groups (Figures 1A, B). These findings show overlapping hematological profiles between DBD subjects and patients with ABL for most measured parameters, with the exception of Hb levels.
Figure 1
Biochemical parameters
No significant differences in liver function markers including AST and ALT were observed between the two groups (Supplementary Figure 1A). However, total bilirubin (TB) and creatinine (Cr) levels were significantly higher in the DBDs than in the patients with ABL (p < 0.05 and p < 0.0001, respectively). These findings suggest that potential liver dysfunction (possibly due to ischemia, hypoperfusion, or hemolysis) and impaired renal clearance (possibly caused by hypovolemia or ischemic injury) may be associated with brain death. Muscle damage markers, such as creatine kinase myocardial band (CK-MB) and myoglobin (Mb), showed no significant differences between the two groups, indicating their similar levels of muscle injury.
Protein analysis revealed that albumin and globulin levels and their ratio (Supplementary Figure 1B) were significantly lower in the patients with ABL than in the DBDs (p < 0.0001 for all comparisons), reflecting protein loss or dilution due to fluid resuscitation. Meanwhile, their lactate dehydrogenase (LDH) levels were comparable, suggesting that cellular turnover or damage rates were similar between the two groups.
Electrolytes
Na, Cl, Ca, and Mg levels were significantly lower in the patients with ABL than in the DBDs (p < 0.0001 for Na, Ca, and Mg; p < 0.01 for Cl; Supplementary Figure 2). These differences might be attributed to fluid loss, electrolyte imbalances, or aggressive fluid resuscitation in the patients with ABL. By contrast, phosphorus (P) levels were significantly higher in the patients with ABL (p < 0.05), possibly reflecting metabolic disturbances or increased cellular turnover during acute hemorrhage. Potassium (K) levels showed no significant differences between the two groups, indicating that potassium homeostasis was preserved in both conditions.
Coagulation and inflammatory markers
Antithrombin III (AT-III) levels showed no significant differences between the two groups (Figure 2), indicating their comparable baseline anticoagulant activity. However, D-dimer levels were markedly elevated in the patients with ABL (p < 0.0001), reflecting increased fibrinolytic activity and possible ongoing coagulopathy in this condition. Fibrinogen (FIB) levels were significantly lower in the patients with ABL (p < 0.0001), possibly due to FIB consumption or hemodilution following aggressive fluid resuscitation. By contrast, C-reactive protein (CRP) levels were significantly higher in the DBDs (p < 0.0001), suggesting a pronounced systemic inflammatory response in brain-dead individuals that is potentially driven by the physiological effects of brain death. As shown in Figure 1A, platelet counts were similar between the two groups, indicating no significant differences in platelet production or consumption under these conditions. These findings highlight that although their AT-III levels and platelet counts are comparable, the observed differences in fibrinolytic activity, FIB depletion, and inflammatory responses underscore distinct physiological characteristics between the DBDs and patients with ABL.
Figure 2
Arterial blood gas test
Significant differences in arterial blood gas parameters were observed between the DBDs and patients with ABL (Supplementary Figure 3). pH levels were significantly lower in the patients with ABL than in the DBDs (p < 0.01), indicating a trend toward acidosis in this group. This finding is supported by their significantly reduced bicarbonate (HCO3-) levels (p < 0.05), reflecting compensatory metabolic acidosis. Lactate (Lac) levels were also significantly elevated in the patients with ABL (p < 0.01), consistent with their increased anaerobic metabolism caused by tissue hypoxia during acute hemorrhage. Partial oxygen pressure (PaO2) was significantly higher in the patients with ABL (p < 0.01), attributed to the oxygen therapy typically administered in response to ABL. Meanwhile, partial carbon dioxide pressure (PaCO2) was significantly lower in the patients with ABL (p < 0.0001), suggestive of compensatory hyperventilation associated with acidosis. These findings illustrate distinct respiratory and metabolic responses between the patients with ABL and DBDs, with the former exhibiting pronounced physiological adaptations to ABL and tissue hypoxia.
Immunological parameters
Total immunoglobulin and complement levels
Significant differences in immunoglobulin and complement levels were observed between the DBDs and patients with ABL (Figure 3). Total IgM, IgG, and IgA levels were significantly higher in the DBDs than in the patients with ABL (p < 0.01, p < 0.05, and p < 0.0001, respectively), although all the average values remained within the normal range. Complement components C3 and C4 were also significantly elevated in the DBDs (p < 0.01 and p < 0.0001, respectively), with average levels falling within normal limits. These findings highlight the differences in baseline immunological parameters between the two groups.
Figure 3
Cytokines
The cytokine profiles differed significantly between the DBDs and patients with ABL (Figure 4). The patients with ABL exhibited significantly higher levels of IL-4, IL-10, IFN-γ, and TNF-α than the DBDs (p < 0.0001 for all). IL-6 levels did not differ significantly between the two groups and were elevated in all the subjects. IL-2 levels, while within the normal range, were significantly higher in the DBDs than in the patients with ABL (p < 0.0001). These findings suggest that the patients with ABL experience a heightened inflammatory state, characterized by elevated proinflammatory (e.g., TNF-α and IFN-γ) and anti-inflammatory (e.g., IL-10) cytokines, which reflects acute immune activation in response to hemorrhage and associated tissue hypoxia. By contrast, the relatively subdued cytokine response in the DBDs may reflect the immunological effects of brain death, which potentially involve immune suppression or dysregulation.
Figure 4
Anti-TKO pig RBC responses
No significant differences in IgM or IgG binding to TKO pig RBCs (Figure 5A) or CDC (Figure 5B) were observed among the groups, including healthy humans, DBDs, and patients with ABL. In addition, no significant variation was found between TKO pig RBCs exposed to sera from the DBDs or patients with ABL. These findings indicate that neither brain death nor ABL significantly affects the IgM or IgG binding and complement-mediated lysis of TKO pig RBCs. The DBDs and patients with ABL exhibited widely distributed values in immune assays, reflecting interindividual variability inherent to clinical populations (Figure 5).
Figure 5
Hemagglutination assays revealed significantly higher agglutination in the sera from the patients with ABL compared with that in the sera from healthy humans and DBDs (p < 0.0001 and p < 0.05, respectively; Figure 5C). Furthermore, hemagglutination in the sera from the DBDs was significantly elevated compared with that in the sera from healthy humans (p < 0.0001). These results suggest that hemagglutination, which reflects anti-RBC antibody-mediated clumping, is heightened under ABL possibly due to altered antibody activity or serum composition.
Overall, these findings indicate that although IgM/IgG binding and CDC were not detectably different between DBD and ABL groups under the conditions tested, hemagglutination was elevated in patients with ABL and warrants further investigation into its underlying mechanisms and potential clinical implications.
Anti-TKO pig RBC responses in patients with ABL categorized by ISS
Immune responses to TKO pig RBCs were assessed in the patients with ABL stratified into the following four groups according to their ISS: minor (n=20, ISS 1–8), moderate (n=42, ISS 9–15), serious (n=23, ISS 16–24), severe (n=17, ISS 25–49), and critical (n=0, ISS 50–74; Supplementary Figure 4). IgM binding, CDC, and hemagglutination levels showed no significant differences across the ISS categories. However, IgG binding was significantly higher in the serious group than in the moderate group (p < 0.01). These findings suggest that although injury severity does not substantially affect IgM binding, CDC, or hemagglutination responses to TKO pig RBCs, the observed increase in IgG binding in the serious group may indicate an immune modulation specific to this severity category. This observation warrants further investigation to clarify its clinical relevance.
Discussion
Xenotransplantation has advanced considerably in recent years, with breakthroughs in the genetic engineering of donor pigs and immunological strategies to prolong graft survival. Although research has focused on organ transplantation (Griffith et al., 2022; Mohiuddin et al., 2023; Kawai et al., 2025), applications have been expanded to tissues and cells such as corneas, pancreatic islets, and RBCs (; ; Yoon et al., 2021; ; Ali et al., 2024; Cooper et al., 2024; ; ). In the field of xenotransfusion, however, progress toward clinical translation has been constrained by the absence of an appropriate human translational reference model for preclinical assessment of GE pig RBCs. In the present study, we addressed this gap by systematically comparing DBD subjects with patients with ABL under standardized institutional and laboratory conditions.
Limitations of NHP models
Despite their widespread use in xenotransplantation, NHPs have substantial limitations when applied to xenotransfusion (Table 3) (Cui et al., 2020; Yamamoto et al., 2020a; ; ). Notably, the knockout of CMAH, the enzyme responsible for Neu5Gc biosynthesis in pigs, appears to unmask a previously cryptic antigen, sometimes referred to as the “4th xenoantigen.” This antigen is recognized by natural antibodies in all OWNHPs but not in all humans (Li et al., 2019; Yamamoto et al., 2020a; ). As a consequence, even TKO pig RBCs that lack Gal, Neu5Gc, and Sda antigens may still be strongly targeted by OWNHP sera (Cui et al., 2020; Yamamoto et al., 2020a), leading to strong complement activation and rapid intravascular clearance of transfused pig RBCs. This immunologic mismatch significantly limits the suitability of OWNHPs (because of consistently positive crossmatch and hemagglutination) for evaluating TKO pig RBCs.
Table 3
| Advantages | Disadvantages | |
|---|---|---|
| Nonhuman primate (NHP) models | Ideal models for short-term studies: They provide a platform for evaluating immune responses and survival of pig RBCs in a controlled setting. Long-term monitoring capability: They allow for extended observation of immune and physiological responses when needed. Extensive experimental data: They are well-established models with significant historical data, ensuring consistency in study design. Bridge toward clinical translation: They have traditionally served as a preclinical bridge toward clinical application. | Differences in immune response and physiology: Immune and physiological discrepancies from humans limit clinical relevance. Limited compatibility of reagents and drugs: Human-specific therapies and reagents may not work effectively in NHPs because of limited cross-reactivity. High cost: Purchase, housing, care, and logistical requirements make NHP studies expensive and less scalable. Ethical concerns: High ethical burden emerges given the sentience and close relation of NHPs to humans. |
| Brain-dead human (DBD) models | Eliminate cross-species discrepancies: They provide a human immune and physiologic background, avoiding the cross-species incompatibilities inherent to NHP models. Allow assessment of selected human inflammatory and immune parameters: They provide a platform for evaluating early immune and inflammatory responses under clinically monitored conditions, although they do not fully reproduce the biologic complexity of acute blood loss. Real-time monitoring under clinical care: They allow precise control and monitoring of experimental variables in a clinical setting. Suitable for short-term translational assessment: Short-term observation in brain-dead donors may be sufficient for evaluating acute compatibility and safety-related responses to pig RBC transfusion. Compatibility with human-directed assays and interventions: They allow assessment using human-compatible assays, reagents, and selected therapeutic approaches under clinically relevant conditions. | Lack of long-term data: Observation periods are short owing to the nature of brain-dead donors. Stable hemodynamics: They do not fully reproduce the pathophysiologic cascade of acute hemorrhage or hemorrhagic shock seen in living patients. Limited donor availability: Logistical barriers reduce feasibility for large-scale studies. High cost: Intensive care and specialized management incur significant expenses, limiting application. Ethical considerations: Consent processes and public concerns present significant challenges for extensive adoption. |
Advantages and disadvantages of different experimental xenotransfusion models.
Recent transfusion studies have illustrated these limitations. showed that TKO pig RBCs expressing human CD55 and CD47 survived less than 2 hours after being transfused into cynomolgus monkeys. found no observable advantage of TKO over wild-type pig RBCs, with hematologic improvements diminishing by day 3 and RBC survival not directly assessed. These findings suggest that even with extensive genetic modifications, pig RBCs cannot overcome the intrinsic immunological incompatibility in NHPs without employing preconditioning regimens. Such desensitization strategies (e.g., antibody depletion or B cell suppression) are incompatible with emergency transfusion scenarios, where immediate crossmatch compatibility is required. Additional logistical and experimental barriers, such as the limited blood volume and small body size of NWNHPs (e.g., capuchin and squirrel monkeys) and the lack of species-specific reagents, further restrict the reproducibility and interpretability of immune assays in NHP models (Yamamoto et al., 2020b). These constraints underscore the need for alternative preclinical models with greater clinical relevance and immunological fidelity to human recipients.
Advantages of brain-dead human models
DBDs offer several advantages as a preclinical model for xenotransfusion (Table 3) (). First, being humans, they eliminate the species-specific immunologic mismatches associated with NHPs (Collins et al., 1995; Zhu and Hurst, 2002; Yeh et al., 2010; Springer et al., 2014; ; Li et al., 2019), allowing for a direct assessment of immunologic compatibility with GE pig RBCs. Second, DBDs can be managed in a controlled clinical setting for standardized sampling, monitoring of physiologic parameters (; ; ; ; ; ; ; ; ), and establishing reproducible assay conditions. These features facilitate direct comparison with transfusion candidates and improve the translatability of findings. Finally, DBDs present systemic inflammation (Cooper and Kobayashi, 2024) and metabolic disturbances that overlap with clinical conditions requiring transfusion, albeit to a different extent than acute trauma.
In our study, DBD subjects and patients with ABL shared some overlapping hematologic and biochemical features, and anti-TKO IgM/IgG binding and CDC were not detectably different between the two groups under the conditions tested. At the same time, several other parameters, including hemagglutination, cytokine profiles, total immunoglobulin levels, and complement components, differed significantly between cohorts. These findings indicate that DBD subjects do not recapitulate the full inflammatory and humoral environment of ABL. Rather than serving as a physiologically equivalent surrogate, the DBD model may be most appropriately viewed as a human translational platform for initial immunologic and safety-oriented assessment in xenotransfusion research.
Interpretation and scientific contribution
This study is the first to comprehensively compare DBDs and patients with ABL under standardized conditions to assess the suitability of DBDs as translational reference models. Our findings support the conclusion that selected early serum-based responses to TKO pig RBCs, particularly IgM/IgG binding and CDC, were not detectably different between these groups under the conditions tested. However, these findings should not be interpreted as demonstrating overall physiologic or immunologic equivalence. Instead, they support the use of DBD subjects as a human translational reference platform for early-phase compatibility and safety-oriented evaluation.
Our data also suggest that the immune and pathophysiologic responses observed in NHP xenotransfusion models may not accurately reflect those in human patients with ABL. For example, studies using cynomolgus monkeys subjected to 25% controlled blood loss reported an expected decrease in Hb and hematocrit levels (Roh et al., 2023; ). However, key inflammatory mediators such as IL-6 did not rise to the levels observed in trauma-related hemorrhage (Roh et al., 2023). This discrepancy reflects differences in etiology: while controlled hemorrhage induces hypovolemia, trauma-induced bleeding involves tissue damage, ischemia–reperfusion injury, and complex immune activation. Although NHP-based ABL models provide controlled experimental conditions, they fail to replicate the clinical and immunologic complexity of human trauma. In clinical ABL scenarios, the delay between traumatic injury and transfusion introduces additional immune and metabolic changes that cannot be modeled by the immediate, volume-controlled hemorrhage protocols in NHPs. This phenomenon underscores the need for complementary models, such as DBDs, which accurately reflect the heterogeneity of clinical transfusion scenarios.
Furthermore, the DBD and ABL cohorts exhibited considerable interindividual variability in antibody binding and CDC responses. This heterogeneity is expected in clinical populations and reflects differences in immune activation, comorbidities, and medical history. Although it adds analytical complexity, it also enhances the generalizability of the findings and mirrors the diversity encountered in real-world transfusion candidates.
Limitations and ethical constraints
Despite their utility, DBDs have inherent limitations (Table 3). They exhibit relatively stable hemodynamics and do not fully replicate the pathophysiologic cascade of hemorrhagic shock, including hypotension, acidosis, and tissue ischemia. In addition, the DBD cohort was not stratified according to the intensity of supportive care, such as vasopressor use or other hemodynamic interventions, which may have influenced measured immune parameters, particularly cytokine profiles. Moreover, ethical considerations preclude interventions such as controlled blood withdrawal beyond 25% of blood volume, which limits the simulation of progressive blood loss. Inflammatory changes in DBDs are primarily neurogenic (e.g., catecholamine surges after brain death) (Cooper and Kobayashi, 2024) and differ in timing and profile from those observed during ABL, which are typically driven by peripheral tissue injury and hypoperfusion. Furthermore, the present study was limited to in vitro immune assays and did not assess in vivo survival of transfused pig RBCs, which remains a critical biological endpoint for xenotransfusion. The present study also did not assess free hemoglobin release outside the CDC assay or evaluate oxidative, mechanical, and osmotic fragility of pig RBCs after exposure to human serum or plasma. These parameters may influence post-transfusion RBC survival and efficacy and should be addressed in future studies. Importantly, this study was not designed as a formal equivalence study. Therefore, the absence of statistically significant differences in selected assays should not be interpreted as proof of equivalence between DBD subjects and patients with ABL. Finally, the ABL cohort consisted exclusively of trauma patients, and the findings may therefore not be fully generalizable to other clinically important causes of ABL, including surgical bleeding and postpartum hemorrhage.
Conclusion
NHPs and brain-dead humans each offer unique strengths and limitations as preclinical models. Although NHPs enable longitudinal studies and organ-level assessments, they suffer from species-specific immunologic mismatches that compromise their utility for evaluating xenogeneic RBCs. By contrast, brain-dead humans provide a human translational reference platform for short-term assessment of immune compatibility and safety-related parameters. Our findings support the use of DBD subjects as a reference model for selected early xenoreactive assays, while also underscoring that they do not fully reproduce the physiologic and immunologic complexity of patients with ABL. Thus, the DBD model may serve as an intermediate bridge between in vitro studies, NHP studies, and future clinical xenotransfusion research.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.
Ethics statement
The studies involving humans were approved by Institutional Review Board (IRB) of the Second Affiliated Hospital of Hainan Medical University (IRB-LW2022901 and IRB-LW2022231). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. The animal studies were approved by Institutional Animal Care and Use Committee approval (IACUC#ZK09-24-01A). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent was obtained from the owners for the participation of their animals in this study.
Author contributions
TL: Project administration, Investigation, Funding acquisition, Conceptualization, Writing – review & editing, Resources, Formal analysis, Visualization, Writing – original draft, Data curation, Methodology, Validation. JL: Formal analysis, Data curation, Writing – review & editing, Writing – original draft. SJ: Data curation, Writing – original draft, Formal analysis, Writing – review & editing. LL: Investigation, Data curation, Resources, Conceptualization, Writing – original draft, Writing – review & editing, Formal analysis. HH: Investigation, Writing – original draft, Resources, Formal analysis, Visualization, Funding acquisition, Conceptualization, Validation, Data curation, Supervision, Methodology, Writing – review & editing. HG: Writing – review & editing. YoW: Writing – review & editing, Methodology, Formal analysis, Data curation, Investigation. YZ: Writing – review & editing. SH: Writing – review & editing. TZ: Writing – review & editing. DP: Resources, Writing – review & editing, Conceptualization. HJ: Writing – review & editing, Data curation, Resources, Conceptualization. YiW: Writing – original draft, Formal analysis, Data curation, Resources, Funding acquisition, Conceptualization.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This study was supported in part by the National Key Research and Development Program (2023YFC3404304: YW, 2024YFC3406800: HJ), National Natural Science Foundation of China (82260154: YW, 82460153: HJ, 82400891: TL) and Hainan Provincial Science and Technology Talent Innovation Project (Category B) (KJRC2023B08: YW), the Academic Enhancement Support Program of Hainan Medical University (XSTS2025029: HH, XSTS2025161: TL), and the Hainan Provincial Natural Science Foundation of China (326MS0417: HH). HH is also supported by the Hainan Provincial High-Level Foreign Experts Recruitment Program (G20250218019E) and the National Foreign Experts Program of China (S20250233). The funder was not involved in the study design, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
Conflict of interest
YoW and DP are employed by Chengdu Clonorgan Biotechnology Co., Ltd. HH is a scientific advisor to PorMedTec, Kawasaki, Kanagawa, Japan.
The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2026.1805268/full#supplementary-material
Glossary
- ABL
acute blood loss
- AT-III
Antithrombin III
- CDC
complement-dependent cytotoxicity
- CRP
C-reactive protein
- CK-MB
creatine kinase myocardial band
- Cr
creatinine
- DBD
brain-dead donors
- FIB
fibrinogen
- Gal
galactose-α1,3-galactose
- GE
genetically engineered
- GTKO
α1,3-galactosyltransferase gene-knockout
- Hb
hemoglobin
- HCT
hematocrit
- ISS
Injury Severity Score
- Lac
Lactate
- LDH
lactate dehydrogenase
- Mb
myoglobin
- Neu5Gc
N-glycolylneuraminic acid
- NHPs
nonhuman primates
- P
phosphorus
- PaCO2
partial carbon dioxide pressure
- PaO2
partial oxygen pressure
- RBCs
red blood cells
- rGM
relative geometric mean
- TB
total bilirubin
- TKO
triple-knockout (GTKO/β4GalNT2KO/CMAHKO)
- WBC
white blood cell
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Summary
Keywords
acute blood loss, brain-dead human, genetically engineered pigs, preclinical models, red blood cells, xenotransfusion
Citation
Li T, Li J, Jin S, Liu L, Hara H, Gan H, Wang Y, Zhang Y, He S, Zhao T, Pan D, Jiang H and Wang Y (2026) Brain-dead humans as preclinical reference models for xenotransfusion: bridging nonhuman primates and clinical applications through in vitro evaluation. Front. Physiol. 17:1805268. doi: 10.3389/fphys.2026.1805268
Received
06 February 2026
Revised
04 May 2026
Accepted
06 May 2026
Published
20 May 2026
Volume
17 - 2026
Edited by
Mauro Magnani, University of Urbino Carlo Bo, Italy
Reviewed by
Vassilis L. Tzounakas, University of Patras, Greece
Ping Li, Indiana University School of Medicine, United States
Meghan Hu, Duke University, United States
Updates
Copyright
© 2026 Li, Li, Jin, Liu, Hara, Gan, Wang, Zhang, He, Zhao, Pan, Jiang and Wang.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hidetaka Hara, harahjp@icloud.com; Hongtao Jiang, jht20032003@163.com; Yi Wang, wayne0108@126.com
†These authors have contributed equally to this work
Disclaimer
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