Red blood cell storage quality and transfusion efficacy enhancement methods, devices, and systems

Abstract

Disclosed herein are devices, compositions, methods, and systems for identifying mammalian cells, especially erythrocytes, with enhanced characteristics, such as hemolysis, stress tolerance, oxygen capacity, and energy metabolism. In some embodiments, the devices, compositions, methods, and systems may be useful in identifying a subject for donating erythrocytes, with enhanced stress resistance, with enhanced physical performance such as oxygen consumption, and strenuous or prolonged physical activities, hemorrhage, or resistance to high altitude hypoxia. Subjects may also be identified for donating cells for use in manufacture of next-generation ex vivo farmed RBCs with optimal storage and resuscitation performances. The disclosed devices, compositions, methods and systems are also useful in the bioengineering of red blood cells and red blood cell precursor ex vivo from hematopoietic stem cells or induced pluripotent stem cells, as well as ithe prediction of cancer progression, severity and responses to therapies targeting glycolysis.

Description

RED BLOOD CELL STORAGE QUALITY AND TRANSFUSION EFFICACY ENHANCEMENT METHODS, DEVICES, AND SYSTEMSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of priority pursuant to 35 U.S.C. § 119(e) of U.S. provisional patent application No. 63 / 519,418 entitled “BIOLOGICAL AND GENETIC FACTORS INFLUENCE GLYCOLYSIS IN HUMAN RED BLOOD CELLSFROM THE 13,091 BLOOD DONORS IN THE REDS RBC OMICS STUDY,’' filed on 14 August 2024, which is hereby incorporated by reference in its entirety.BACKGROUND

[0002] Red blood cells (RBCs) account for -83% of all the cells in the human body. Ease of collection and shear abundance of RBCs have made them an eligible model for the investigation of metabolism in the early days of biochemistry. Unlike other vertebrates, mammalian RBCs have evolved to maximize oxygen carrying capacity by increasing hemoglobin content per cell, a feature they achieved by losing nuclei, organelles, and the ability to synthesize proteins de novo. Given the absence of de novo protein synthesis capacity, RBC rely on dynamic metabolic reprogramming in response to environmental stimuli, including hypoxia or oxidant stress (e.g.. at altitude or during exercise). For example, small molecule metabolites like adenosine triphosphate (ATP) play a role in hemoglobin allostery by stabilizing the tense deoxygenated state and promoting oxygen release to tissues. Mitochondria-devoid RBCs represent a simplified model of eukaryotic cell metabolism, a cell type that exclusively relies on glycolysis as the main source of energy for the synthesis of ATP. For the reasons above, is not a coincidence that earliest efforts to reconstruct human metabolic networks in silico have been centered on RBC metabolism.

[0003] What is needed, is a more detailed understanding of RBC metabolism and an ability to optimize RBC storage conditions, predict post-transfusion performance of RBC, and / or identify cells and blood units with enhanced characteristics.SUMMARY

[0004] Disclosed herein are methods of selecting a RBCs. erythrocytes, or population thereof, wherein the RBCs possess enhanced characteristics related to storage or posttransfusion, as well as other stress-related characteristics such as tolerance to exercise, toxic chemicals such as chemotherapy, irradiation, disease, etc. In most cases, the RBCs may be stored for longer periods and undergo transfusion with lower rates of hymolysis. In some embodiments, the disclosed methods may be used to identify cells with enhanced characteristics, such as stem or progenitor cells for ex -vivo production, such as for manufacture of RBCs with optimal storage and resuscitation performance. In many embodiments, the methods comprisingsteps of assaying the RBC for one or more biomarkers, for example ATP, hypoxanthine, and lactate. In many embodiments, the enhanced RBCs may have ATP and / or lactate levels that are higer than about 90% of the comparator RBCs from various donors and / or may have hypoxanthine levels lower than about 90% of comparator RBCs.

[0005] Also disclosed are methods of determining a characteristic of an RBC or erythrocyte from a subject, comprising determining whether the subject’s genome includes at least one genetic marker, wherein the genetic marker is located at or near a gene in the glycolytic pathway. In some embodiments, the at least one genetic marker is a single nucleotide polymorphism selected from rs2388595, rs2388594, rs56882221. rs34538474, rs!0903962, rs872398. rs7914782, rs!2249060, rsl !592619, rs55845900, rs597808, rs2072931, rs9438900, rs609320, rs586178, rs9891699, rs2542591, rs2542590, rs2542573, rs2692513, rs2692512, rs4791641, rsl059476, rsl 1078738, rs805385, rs2241909, rs3826543, rs2949815, rsl050828, rsl984564, rs3764031, rs73264607, rs!74568. rs!74537, rsl02274, rsl74561, rs!6933078, rs!2210538, and rs8052118. In some embodiments, the erythrocyte’s characteristics may enhanced if the subject’s genome includes a SNP selected from rs2388595, rs2388594, rs56882221, rs34538474, rs!0903962, rs872398, rs7914782, rsl2249060, rs! 1592619, rs55845900, rs597808, rs2072931, rs9438900, rs609320, and rs586178; and / or excludes a SNP selected from rs9891699, rs2542591, rs2542590. rs2542573. rs2692513, rs2692512, rs4791641, rsl059476, rsl lO78738, rs805385, rs2241909, rs3826543, rs2949815, rsl050828, rsl984564, rs3764031, rs73264607, rsl74568, rsl74537, rs!02274, rsl74561, rs!6933078, rsl2210538, and rs8052118.

[0006] Array’s for identifying a subject with superior blood donor properties are also disclosed. In most embodiments, the arrays consist of oligonucleotides selected from rs2388595, rs2388594. rs56882221, rs34538474, rs!0903962. rs872398, rs7914782, rsl2249060, rsl 1592619, rs55845900, rs597808, rs2072931 , rs9438900, rs609320, rs586178, rs9891699, rs2542591, rs2542590, rs2542573, rs2692513, rs2692512, rs4791641, rsl059476, rsl 1078738, rs805385, rs2241909, rs3826543, rs2949815, rsl050828, rsl984564, rs3764031, rs73264607, rsl74568, rsl74537, rsl02274, rsl74561, rsl6933078, rsl2210538. and rs8052118, or oligonucleotides that hybridize to same.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0008] Figures 1A-I - Metabolomics of 13,091 end of storage packed RBCs from the REDS RBC Omics Index cohort Leukocyte-filtered RBCs were donated by 13,091 volunteers across four different blood centers in the United States and stored for 42 days (end of the shelf-life)prior to testing via high-throughput metabolomics. In FIG. 1A, an overview of the study design and donor demographics. In FIGs. 1B-E, linear discriminant analysis of all the samples as a function of donor age, sex, body mass index (BMI) and ethnicity, respectively. In FIG. IF, Uniform Manifold Approximation and Projection (uMAP), color-coded as a function of 10 main K mean clusters. In FIG. 1G. network view of metabolites vs demographics across the whole population. In FIG. 1H, network view of metabolite-metabolite correlations (Spearman) as a function of the 10 K means clusters. Each metabolite is a node and a red and blue edge indicates positive or negative correlations higher than 0.5 in module. In FIG. II, heat map of the top 50 significant metabolites that discriminate the 10 clusters identifies packed RBCs from donors in cluster 4 as the group with the highest glycolytic intermediates and products, including ATP.

[0009] Figures 2A-H - Donors in cluster 4 are characterized by elevation in glycolysis. In FIG. 2A, overview of glycolysis. In FIG. 2B, elevated day 42 adenosine triphosphate (ATP) is observed in pRBCs from donors in cluster 4, despite overall decreases of ATP as a function of storage duration (e.g., in 643 index donors donated a second unit of blood that was sampled at storage day 10, 23 and 42 as shown in FIG. 2C). Significant metabolic changes are observed between donors as a function of ATP levels (5th vs 95th percentile - FIG. 2D), with elevated ATP positively correlating with increases in all glycolytic intermediates (Spearman - FIG. 2E). In FIG. 2F. ridge plots for all glycolytic intermediates show elevation of hexoses, hexose phosphate and triose phosphates all the way to pyruvate in donors from cluster 4. In FIG. 2G, uMAP color coded by ATP levels (also used for the z axis). In FIG. 2H, donors in cluster 4 have elevated ATP levels compared to the other 9 clusters independently from their age.

[0010] Figures 3A-D - Glycolysis is impacted by donor sex, age, ethnicity and BMI. Line plots (median + quartile - Loess smoothing algorithm) as a function of donor age, alone (FIG. 3A) or in combination with sex (FIG. 3B), BMI (FIG. 3C) and ethnicity’ (FIG. 3D).

[0011] Figures 4A-J - Genetic underpinnings of glycolytic heterogeneity in end of storage units from 13,091 REDS RBC Omics Index donors. Along with metabolomics analyses, as part of the REDS RBC Omics study donors were tested for -879,000 single nucleotide polymorphisms (SNPs - FIG. 4A). A summary Manhattan plot of metabolite Quantitative Trait Loci (mQTL) is shown in FIG. 4B for all glycolytic metabolites merged, or just for ATP (FIG. 4C). For both ATP (FIG. 4D) and lactate (Manhattan plot in FIG. 4E, locus Zoom in FIG. 4F). SNPs on phosphofructokinase P (PFKP) were associated to the end of storage levels of these metabolites. Similar observations were made for hypoxanthine (FIG. 4G), also significantly impacted by PFKP SNPs (QQQ plot in FIG. 4H), and a breakdown product of ATP (FIG. 41). In FIG. 4J, summary7table of the most significant SNP hits mapping on PFKP for all glycolytic metabiolites, ATP and hypoxanthine.

[0012] Figures 5A-H - Manhattan plots of glycolytic metabolites upon mQTL analysis of 13,091 end of storage packed RBCs from the REDS RBC Omics Index cohort. Manhattan plots for all glycolytic intermediates and ribose phosphate - end product of the oxidative phase of the pentose phosphate pathway (FIGs. 5A-H).

[0013] Figures 6A-D - Metabolic correlates to PFKP SNPs. Correlation analysis of PFKP SNP dosage across donors confirms a strong positive association with lactate (FIG. 6A) and ATP levels (FIG. 6B) and negative association with hypoxanthine (FIG. 6C). SNP dosage was associated with increases in all glycolytic intermediates and increased consumption of glucose (FIG. 6D).

[0014] Figures 7A-D - Positive and negative association of Glycolysis / ATP and hypoxanthine to in vitro and in vivo hemolysis in autologous healthy and non-autologous critically ill recipients requiring transfusion. End of storage spontaneous hemolysis - one of the two FDA gold standard for the quality of stored blood products - was measured in all 13.091 units. Lactate levels at storage day 42 were the top predictor of spontaneous hemolysis (FIG.7 A). In a separate cohort of 79 volunteer donations and autologous transfusion events, glycolytic metabolites and ATP levels were positively associated to, and hypoxanthine negatively associated to measurements of post-transfusion recovery with 51 Cr in healthy autologous volunteers - the second and final FDA gold standard to monitor the quality of stored RBCs (FIG. 7B). By accessing thec'vein-to-vein’’ database of the REDS RBC Omics program, end of storage ATP and hypoxanthine levels were linked to hemoglobin increments (n=5,816 records available in the database) and bilirubin increments (n=914) upon heterologous transfusion of products from the same donors to critically ill recipients requiring transfusion (FIGs. 7C-D).

[0015] Figure 8 Ridge plots of citrate and mannitol by K means cluster of the 13,091 REDS RBC Omics Index donors. Clusters 3, 6, 7 and 8 showed a subpopulation with higher levels of mannitol, while citrate was the highest in clusters 4-5, suggesting that the former group was stored in mannitol containing additives (AS-1 for this study) and the latter in mannitol-free additives with higher concentrations of citrate (AS-3 in this study).

[0016] Figure 9 Rainfall plots and uMAP of the 13,091 donors for ATP and hypoxanthine. In the uMAP panels (bottom quadrants), the z axis is either showing normalized ATP (left) or hypoxanthine (right).

[0017] Figure 10 Metabolic differences in RBCs from donors with extreme (n=700) highest vs lowest ATP. The left panel shows a volcano plot of the differences between the two groups with high vs low ATP (ATP is excluded from the plot). In the panel on the right, heat map of the top 40 metabolites by T-test betw een the two groups.

[0018] Figure 11 Metabolic differences in RBCs from donors with extreme (n=700) highest vs lowest hypoxanthine. The left panel shows a volcano plot of the differences between the two groups with high vs low hypoxanthine (hypoxanthine is excluded from the plot). In the panel on the right, heat map of the top 40 metabolites by T-test between the two groups.

[0019] Figure 12 Metabolic differences in RBCs from donors with extreme (n=700) highest vs lowest hypoxanthine and concomitantly lowest vs highest ATP. The left panel shows a volcano plot of the differences between the two groups with high vs low hypoxanthine and low vs high ATP. In the panel on the right, heat map of the top 40 metabolites by T-test between the two groups.

[0020] Figure 13 Combined Manhattan plots for all the significant SNPs across all glycolytic metabolites as determined by mQTL analysis of 13,091 REDS RBC Omics Index donors

[0021] Figure 14 mQTL analysis of ATP. mQTL analyses were performed for ATP and identified HBS1L and PFKP SNPs as significant (QQQ plot on the left panel, bottom row). Locus Zoom for the PFKP coding region in the right panel on the bottom row.

[0022] Figure 15 Locus Zoom for all the significant SNPs for PFKP and HK1 across all glycolytic metabolites as determined by mQTL analysis of 13,091 REDS RBC Omics Index donors

[0023] Figure 16 Metabolic correlates to the degree of vesiculation in 643 units from the REDS RBC Omics Recalled donor population (i.e., a subset of donors with extreme hemolytic propensity - 5th and 95th percentile of the total Index population).DETAILED DESCRIPTION

[0024] Red blood cells (RBCs) account for -83% of all the cells in the human body. Ease of collection and shear abundance of RBCs have made them an eligible model for the investigation of metabolism in the early days of biochemistry. Unlike other vertebrates, mammalian RBCs have evolved to maximize oxygen carrying capacity by increasing hemoglobin content per cell, a feature they achieved by losing nuclei and organelles. Given the absence of de novo protein synthesis capacity, RBC rely on dynamic metabolic reprogramming in response to environmental stimuli, including hypoxia or oxidant stress (e.g., at altitude or during exercise). For example, small molecule metabolites like adenosine triphosphate (ATP) play a mechanisticrole in hemoglobin allostery by stabilizing the tense deoxygenated state and promoting oxygen release to tissues. Mitochondria devoid RBCs represent a simplified model of eukaryotic cell metabolism, a cell type that exclusively relies on glycolysis as the main source of energy for the synthesis of ATP. For the reasons above, it is not a coincidence that the earliestefforts to reconstruct human metabolic networks in silico have been centered on RBC metabolism.

[0025] Understanding RBC metabolism may be useful in optimizing storage conditions and predicting post-transfusion performance of blood products. Transfusion of packed RBCs is the most common in hospital medical procedure after vaccination, a life-saving procedure for tens of millions of civilian and military patients whose survival depends solely on chronic or massive transfusion, with no current alternative iatrogenic interventions. Altered energy' metabolism is a hallmark of the so-called storage lesion, a series of biochemical and morphological alterations that ultimately increase the propensity of stored RBCs to hemolyze in the unit or upon transfusion. Rapid depletion of ATP pools, storage-dependent depletion of glucose and accumulation of lactate are biomarkers of the metabolic storage lesion, though the clinical relevance of such lesion is unclear. RBC metabolism under refrigerated storage conditions is impacted by storage temperatures, additive solutions, dietary or other exposures (e.g., smoking, alcohol consumption, drugs that are not grounds for blood donor deferral), but also by factors such as donor age, sex, ethnicity and body mass index (BMI). In other words, blood is a biological product derived from the altruistic gift of millions of volunteers around the world every year and, as such, its quality, storability and post-transfusion performance are tied to donor genetics and non-genetic factors.

[0026] The recent implementation of high-throughput and cost-effective genomics and metabolomics platform has favored the burgeoning of large scale metabolic Quantitative Trait Loci (mQTL) studies. This new body of work has expanded Garrod’s classic concept of chemical individuality, through comprehensive genome-wide association studies that identified novel genetic variants contributing to variation in blood metabolite levels. Most of these studies have delved into the genetic underpinnings of pathological metabolic conditions. These efforts underlie the foundation of modern-day clinical chemistry, especially the branch focusing on inborn errors of metabolism, such as those linked to hemolytic disorders (e.g., pyruvate kinase deficiency or glucose 6-phosphate dehydrogenase deficiency).

[0027] Applicants show that mQTL studies are useful in the field of transfusion medicine. Specifically, through unsupervised analyses Applicants have surprisingly identified the glycolysis pathway as a pathway that informs clustering of donors as a function of their biology (sex, age. ethnicity, BMI) and genetics. These results are correlated to RBC hemolytic propensity- in-vitro and in-vivo, upon transfusion of stored RBCs in healthy autologous recipients and - through a state-of-the art vein-to-vein database - in thousands of clinical patients receiving transfusion.

[0028] In recent years, strategies have been introduced to bioengineer red blood cells and red blood cell precursor ex vivo from hematopoietic stem cells or induced pluripotent stem cells. Generating RBCs with optimal glycolytic capacity of ex vivo generated RBCs can boost both the proliferation / expansion phase, as well as the efficacy (i.e., ATP synthesis and oxygen offloading) and storability of the end product. Applicants identify genetic polymorphisms that maximize RBC glycolytic potential for these purposes.

[0029] As glycolytic reprogramming is a hallmark of most tumors, this invention identifies genetic signature (single nucleotide polymorphisms) associated with elevated glycolysis in human cells, thus making the genetic signatures at the center of the present disclosure suitable markers for the prediction of cancer progression, severity and responses to therapies targeting glycolysis.

[0030] Surprisingly, Applicants have identified specific bio and genetic markers the correlate with with various characteristics such as stability in storage, post-perfusion resistance to hemolysis, stress tolerance, oxygen capacity, energy metabolism, etc. The disclosed markers may be used to identity' cells with enhanced characteristics, or identify, and in some cases avoid use of, cells having less desirable characteristics. At the same time, these genetic signatures can be used to design optimal ex vivo engineered artificial red blood cells from any stem cell source (e.g., hematopoietic stem cells, induced pluripotent stem cells). The inclusion of such polymorphisms would make artificial RBC precursors easier to culture (i.e., faster proliferation because of enhanced glycolysis), store better because of improved energy metabolism and decreased susceptibility' to lipid peroxidation and other oxidative stress markers.

[0031] Disclosed herein are subjects with with enhanced characteristics related to storage and use of blood. In many embodiments, the subjects may be identified by assessing genetic markers, in one example polymorphic markers, for example small nuclear polymorphisms or SNPs. In many embodiments, the genetic markers may be located at, near, or in the locus of a gene. The disclosed polymorphisms may alter coding or non-coding sequences and / or affect regulation, expression, splicing, or post-translational processing. In some embodiments, the genes may be associated with glycolysis and / or the glycolytic pathway. In many embodiments, the genes may be selected from genes presented at FIGs. 4 and 5. In some embodiments, the one or more genetic markers may be associated with one or more of the genes ACYP2, ATXN2, AURKB, BST1, CIS, CPPED1, CTC1, ERLEC1. FADS 1, FADS2, G6PD. HBS1L, HK1 LPCAT3, MYRF, PF AS, PFKP. PHB2, PKLR, PSME4, RHCE, PGM2L1. SLC22A16, SLC7A5, or TMEM258. In most embodiments, the genetic marker is located at or near the location of PFKP. In many embodiments, the genetic marker may be selected from the markers in Figure 4J.

[0032] The disclosed cells and subjects may possess or lack one or more SNPs. In most embodiments, the SNP may be one or more of rs2388595, rs2388594, rs56882221, rs34538474, rs!0903962, rs872398, rs7914782, rsl2249060, rsl 1592619, rs55845900, rs597808, rs2072931, rs9438900, rs609320, rs586178, rs9891699, rs2542591, rs2542590. rs2542573, rs2692513, rs2692512. rs4791641. rsl059476. rsl 1078738, rs805385, rs2241909, rs3826543, rs2949815, rsl050828, rsl984564, rs3764031, rs73264607, rsl74568, rsl74537, rsl02274, rsl74561, rsl6933078, rsl2210538, and rs8052118. SNPs, designated by “rs” numbers, are well known to those of skill in the art, and can be obtained from various sources including the National Library of Medicine's SNP database. dbSNP at ncbi.nlm.nih.gov / snp / .

[0033] Disclosed herein are cells with enhanced characteristics related to storage and perfusion. As used herein, perfusion may include autologous and non-autologous perfusion, reperfusion, and tranfusion. The disclosed cells may be obtained, derived, or genetically engineered. In some embodiments, the cells may be obtained or derived from the subjects described above. In other embodiments, the cells may be engineered to include one or more polymorphisms as described above.

[0034] The disclosed cells may possess and may be identified by one or more biomarkers. In some embodiments, the biomarker may be a product, intermediate, or metabolite of glycolysis or the glycolytic pathway. In many embodiments, the biomarker may be selected from ATP, hypoxanthine, or lactate. Disclosed herein are cellular compositions and biomarkers useful in identifying cells and subjects having enhanced characteristics related to storage and use of red blood cells. In many embodiments, the biomarkers are intermediates and products of a metabolic pathway, for example glycolysis. In some embodiments, a biomarker may be selected from the intermediates and products shown at Figure 2A, in some embodiments one biomarker may be adenosine triphosphate (ATP), hypoxanthine, or lactate.

[0035] Disclosed herein are cells with enhanced characteristics in terms of blood storage and use. The enhanced cells possess biomarker levels significantly different than cells with less desirable, average, or poor characteristics. In some embodiments, the levels may be elevated. In some embodiments, the levels may be lowered. In some embodiments, the levels of some biomarkers in the disclosed cells may be elevated while other biomarker levels low ered. In some embodiments, such as where the biomarker is hypoxanthine, the hypoxanthine levels may be lower than the average for other cells. In many embodiments, for example where the biomarker is ATP. the disclosed red blood cells may possess elevated adenosine triphosphate (ATP) levels relative to other cells. In many embodiments, the disclosed cells may have high levels of ATP and low' levels of hypoxanthine.

[0036] The disclosed cells have biomarker levels that are very high and / or very low relative to cells of average characteristics. In many embodiments, the biomarker level may' be at or above the 90th, 91st, 92nd, 93rd, 94th, 95th, 96th, 97th, 98th, or 99thpercentile of similar cells, and / or lower than the 10th. 9th. 8th, 7th, 6th, 5th, 4th, 3rd, 2nd, or 1stpercentile - for one example, where the disclosed cells are RBCs, they may possess ATP levels higher than the 90thpercentile when compared with a random sampling of RBCs from various donors or subjects.

[0037] Biomarker levels of the disclosed cells may be assessed at various times, for one example after 10 or more days of storage, for example more than 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29. 30. 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or 42 days and less than about 42. 41. 40. 39, 38, 37, 36, 35, 34, 33, 32, 31, 30. 29. 28. 27. 26. 25. 24. 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, or 11 days.

[0038] Methods and processes for identifying cells or donors of same possessing characteristics that enhance storage stability are disclosed. In various embodiments, the disclosed methods and processes include assessing the cells for various biomarkers and / or genetic markers. In many embodiments if the biomarker level is greater than 90% percent of the levels in other cells, or less than 90% of all other cells, the cell may be identified as a cell with enhanced characteristics.

[0039] The disclosed methods may be useful in identifying or selecting donors with cells having enhanced characteristics - i.e. in the case of blood donation they may be "super"-donors. In one embodiment, RBCs from super-donors are characterized by elevated glycolysis and ATP levels, and low hypoxanthine levels. While ATP-depleted ery throcytes may be rapidly removed from the bloodstream via mechanisms of intra- or extravascular hemolysis, the disclosed cells may be longer lived in the bloodstream and ex-vivo, i.e. when stored.

[0040] Genetic markers are also disclosed that indicate a subject or pre-cursor cell may possess enhanced characteristics. Applicants show that, in addition to PFKP and HK being possible rate-limiting enzymes of glycolysis, their altered expression or function, resulting from genetic polymorphisms affect RBC metabolism during storage as well as hemolysis in vitro and in vivo.

[0041] In some embodiments, a biological sample is obtained from the subject. In some embodiments, the biological sample obtained from the subject may be a cell, for example, an erythrocyte from a unit of previously obtained and processed blood.

[0042] The term “biological sample’7refers to a sample derived from a subject, e.g., a patient. Biological samples include, but are not limited to tissue (e.g., skin tissue), cerebrospinal fluid, blood, blood fractions (e.g., serum, plasma), sputum, fine needle biopsy samples, urine, peritoneal fluid, and pleural fluid, or cells therefrom (e.g., blood cells (e.g., white blood cells, redblood cells, erythrocytes)). Accordingly, a biological sample may comprise a tissue, cell or biomolecule (e.g., RNA, protein, metabolite, etc). In some embodiments, the biological sample is a sample of peripheral blood, serum, cerebrospinal fluid, urine and tissue.

[0043] It is to be understood that a biological sample may be processed in any appropriate manner to facilitate determining levels of various biomolecules and / or the presence or absence of a genetic marker. For example, biochemical, mechanical and / or thermal processing methods may be appropriately used to isolate a biomolecule of interest, e.g., ATP or nucleic acid, from a biological sample. A biomolecule sample may be isolated from a clinical sample by processing the biological sample using methods well known in the art and levels of the biomolecule may be determined in the biological sample.

[0044] Further aspects of the present disclosure include an array comprising oligonucleotide probes that hybridize to at least one RBC-characteristic SNPs, which are useful for determining the presence or absence of multiple nucleic acids simultaneously. In some embodiments, the nucleic acid arrays comprise, or consist essentially of. binding probes for SNPs of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 1 1, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25. at least 26, at least 'Ll, at least 28, at least 29, at least 30, at least 31, at least 32. at least 33. at least 34, at least 35, at least 36, at least 37, at least 38 or all 39 of SNPs listed in TABLE 1: rs2388595, rs2388594, rs56882221, rs34538474, rsl0903962, rs872398, rs7914782, rsl2249060, rsl 1592619, rs55845900, rs597808, rs2072931, rs9438900, rs609320, rs586178, rs9891699, rs2542591, rs2542590, rs2542573, rs2692513, rs2692512. rs4791641, rsl059476, rsl 1078738. rs805385, rs2241909, rs3826543. rs2949815. rs!050828. rsl984564, rs3764031, rs73264607. rs!74568, rs!74537. rsl 02274, rsl 74561, rs l 6933078, rsl 2210538, and rs80521 18. Such arrays may be obtained or produced from commercial sources. Methods for producing nucleic acid arrays are well known in the art. For example, nucleic acid arrays may be constructed by immobilizing to a solid support large numbers of oligonucleotides, polynucleotides, or cDNAs capable of hybridizing to nucleic acids corresponding to SNP, or portions thereof. The skilled artisan is also referred to Chapter 22 “Nucleic Acid Arrays” of Current Protocols In Molecular Biology (Eds. Ausubel et al. John Wiley and #38; Sons NY, 2000).

[0045] Kits comprising reagents for detecting the presence or absence of at least one enhanced RBC-characteristic from the biological sample are also provided. In some embodiments, the kit may include reagents to detect 2, 3 or more of the biomarkers described herein, for example ATP, hypoxanthine, and lactate. In additional embodiments, the kit may include reagents to detect 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17. 18, 19, 20, 21, 22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33. 34. 35. 36. 37. 38, or 39 of SNPs rs2388595, rs2388594, rs56882221, rs34538474, rsl0903962, rs872398, rs7914782, rsl2249060, rsl 1592619, rs55845900, rs597808, rs2072931, rs9438900, rs609320, rs586178, rs9891699, rs2542591, rs2542590, rs2542573. rs2692513, rs2692512, rs4791641, rsl059476, rsl 1078738, rs805385, rs2241909, rs3826543, rs2949815, rsl050828, rsl984564, rs3764031, rs73264607, rsl74568, rsl74537, rsl02274, rsl74561, rsl6933078, rsl2210538, and rs8052118. In further embodiments, the kit may include at least 39 nucleic acids to detect all 39 of the SNPs. Kits may include a package housing one or more containers with reagent for measuring the presence of at least one biomarker of genetic marker from the biological sample and instructions for determining the level, presence, or absence thereof. Kits comprising the nucleic acid-based assays, including oligonucleotide arrays described herein are also included.

[0046] Prior to the present studies, the impact of biological and / or genetic factors on energy metabolism in stored erythrocytes, had not been appreciated. RBC storage is not only a clinically relevant manipulation of the erythrocyte, but also represents a form of stress (especially, oxidative stress) that at least in part recapitulates other biologically relevant stressors, such as exercise, exposure to drugs for example during chemotherapy (e.g., anthracy clines like doxorubicin), irradiation, or disease (e g., hemoglobinopathies like sickle cell disease or chronic kidney disease).

[0047] Applicants show that the cells with the presently disclosed characteristics perform better after storage. The disclosed methods are useful in identifying cells with decreased hemolytic propensity7, and improved reperfusion performance. The disclosed methods and markers are also useful in creating engineered cells with enhanced characteristics. In some cases this may be accomplished by identifying donors of hematopoietic or induced-pluripotent stem cell for manufacture of next-generation ex vivo farmed RBCs with optimal storage and resuscitation performances. The disclosed methods and markers may be useful in the identification of optimal starting genotypes for blood group and rare antigen compatibility. The disclosed methods and markers may also be useful in identifying subjects prior to the subject facing challenges requiring increased oxygen consumption, such as exposure to high altitude hypoxia, strenuous or prolonged physical activities or hemorrhage.

[0048] Herein is described a large metabolomics study of stored RBCs from 13,091 donor volunteers. Results from this study show that end of storage units are metabolically heterogenous, but a cluster of "supe -donors is identified. Applicants show that RBCs from super-donors are characterized by elevated glycolysis and ATP levels, and low hypoxanthine levels. Without wishing to be narrowed by theory7, this may be relevant in that ATP fuels important processes in mature RBCs, including: hemoglobin allostery to metabolism, fromproton pumps to membrane integrity via phosphorylation of structural proteins, from protein stabilization via fueling of transglutaminase 2 to cellular mechanics, from cytoskeletal actin polymerization to vesiculation, from membrane lipid symmetry7by fueling phosphatidylserine flippases to proteasomal activity to remove damaged proteins. It has long been posited that the ATP-depleted erythrocyte is rapidly removed from the bloodstream via mechanisms of intra- or extravascular hemolysis, such as sequestration in the splenic slit and erythrophagocytosis by residential macrophages in the reticuloendothelial system.

[0049] Specifically, in the present study Applicants show that age is a negative contributor to glycolysis, which is higher in male, non-obese donors compared to pre-menopausal and obese females. Upon appreciation of ethnicity -based differences in glycolytic rates - as inferred from steady state measurements - Applicants identified common polymorphisms in PFKP (followed by HK1) as significant contributors to glycolytic heterogeneity in human blood donor volunteers. While PFK and HK are rate-limiting enzymes of glycolysis, previous work has not shown or suggested the coding regions for these genes are polymorphic in healthy humans, or that these polymorphisms affect stored RBC metabolism and / or influence hemolysis in vitro and in vivo, upon perfusion in autologous healthy volunteers and thousands of non-autologous critically ill patients requiring transfusion. Of note, the units tested here were all leukocyte (log4.5) and platelet (log2.5) filtered, making the PFKP hit unlikely to be the result of an enrichment in platelets in a subset of units.

[0050] Small scale studies in twins suggested heritability of RBC ATP levels. However, no study has disclosed, suggested, or addressed the impact of biological and / or genetic factors influencing energy metabolism in stored erythrocytes. RBC storage is not only a clinically relevant manipulation of the erythrocyte, but also a form of stress (especially, oxidative stress) that at least in part recapitulates other biologically relevant stressors, such as exercise, exposure to drugs for example during chemotherapy (e.g., anthracy clines like doxorubicin), irradiation, or disease (e.g., hemoglobinopathies like sickle cell disease or chronic kidney disease).

[0051] Applicants reviewed a massive vein-to-vein database, to compare the present findings to clinical data from decades of studies. This data was consistent with a role for ATP levels in the regulation of RBC hemolytic propensity, especially extravascular hemolysis upon transfusion. Similarly, it had been recently reported that hypoxanthine accumulation as a function of adenosine or AMP deaminase activation during storage is a marker of the metabolic storage lesion that predicts extravascular hemolysis in mice and humans. Here Applicants confirm this laboratory evidence with autologous PTR studies, and provide the first clinical data in support to these findings in thousands of transfusion recipients and vein-to-vein database analyses. Disclosed herein is the identification of advantageous polymorphisms in thedetermination of "super ‘-donors not just with respect to decreased hemolytic propensity, but also improved reperfusion performances in vivo in patients requiring transfusion. Also disclosed are processes for manufacture of ex vivo farmed RBCs from hematopoietic or induced-pluripotent stem cell Applicant’s results are useful in the identification of optimal starting genotypes not just with respect to blood group and rare antigen compatibility, but also with boosted energy metabolism could inform the engineering of ex vivo farmed blood units with optimal storage and resuscitation performances. These considerations may also hold true when considering other relevant civilian or military settings (e.g., athletes, soldiers), for example for the selection of soldiers facing challenges requiring increased oxygen consumption, such as exposure to high altitude hypoxia, strenuous or prolonged physical activities or hemorrhage.

[0052] Finally, RBCs and well-curated cohorts of repeated healthy blood donors represent a perfect model to investigate glycolysis at the population levels. Indeed, RBCs are a cell type that exclusively relies on glycolysis to meet its energy demands. Moreover, blood storage is a clinically-relevant stress-intervention that exacerbates the reliance on glycolytic fluxes in the absence of compensatory mechanisms from other organs in vivo. We argue that lessons learned from these studies are translationally relevant for many other research endeavors. Indeed, metabolic reprogramming towards glycolysis - often referred to as Warburg effect in the context of cancer - is perhaps one of the hottest areas of research in the last two decades. Glycolytic reprogramming has been linked as one of the etiological contributors to many diseases, from cancer to cardiovascular disease, from immunometabolism to aging and age-related neurodegenerative diseases. Our findings suggest that the severity of glycolytic reprogramming in humans - e.g., upon accumulation of mutations to oncogenes / tumor suppressor genes or in response to other factors (e.g., inflammation) - may be mediated by the same biological and genetic factors that regulate glycolysis in human RBCs as described in the present study, making our findings relevant to other disciplines beyond transfusion medicine.EXAMPLESExample 1 - Methods

[0053] Donor recruitment in the REDS RBC Omics study: Index donors A total of 13,758 donors were enrolled in the Recipient Epidemiology and Donor evaluation Study (REDS) RBC Omics at four different blood centers across the United States (biolincc.nhlbi.nih.gov / studies / reds_iii / ). Of these, 97% (13,403) provided informed consent and 13,091 were available for metabolomics analyses in this study - henceforth referred to as “index donors”. A subset of these donors were evaluable for hemolysis parameters, including spontaneous (n=12,753) and stress (oxidative and osmotic) hemolysis analysis (n=10,476 and 12,799, respectively) in ~42-day stored leukocyte-filtered packed RBCs derived from wholeblood donations from this cohort. Methods for the determination of FDA-standard spontaneous (storage) hemolysis test, osmotic hemolysis (pink test) and oxidative hemolysis upon challenge with AAPH have been extensively described elsewhere.

[0054] High-throughput metabolomics Metabolomics extraction and analyses in 96 wellplate format were performed as extensively described in previous studies. RBC samples were transferred on ice on 96 well plate and frozen at -80 °C at Vitalant San Francisco prior to shipment in dry ice to the University of Colorado Anschutz Medical Campus. Plates were thawed on ice then a 10 uL aliquot was transferred with a multi-channel pipettor to 96-well extraction plates. A volume of 90 uL of ice cold 5:3:2 MeOH:MeCN: water (y / v / v) was added to each well, with an electronically -assisted cycle of sample mixing repeated three times. Positive pressure was then applied via a 96-well plate manifold viaN2, which facilitated sample extract release into the sample plate. Each plate was covered with a sealed mat and transferred to an ultra-high-pressure liquid chromatography (UHPLC-MS — Vanquish) equipped with a plate charger. A blank containing a mix of standards detailed before and a quality control sample (the same across all plates) were injected 5 times each per plate, and used to monitor instrument performance through the analysis. Metabolites w ere resolved on a Phenomenex Kinetex C18 column (2.1 x 150 mm, 1.7 um) at 45 °C using a 1-minute ballistic gradient method in positive and negative ion modes (separate runs) over the scan range 65-975 m / z exactly as previously described. The UHPLC was coupled online to a Q Exactive mass spectrometer (Thermo Fisher). The Q Exactive MS was operated in negative ion mode, scanning in Full MS mode (2 qscans) from 90 to 900 m / z at 70,000 resolution, with 4 kV spray voltage, 45 sheath gas, 15 auxiliary gas. Following data acquisition, raw files were converted to .mzXML using RawConverter then metabolites assigned and peaks integrated using ElMaven (Elucidata) in conjunction with an inhouse standard library.

[0055] mQTL analysis The workflow' for the mQTL analysis of ATP, hypoxanthine and glycolytic metabolites is consistent with previously described methods from our pilot mQTL study on 250 recalled donors. Details of the genotyping and imputation of the RBC Omics study participants have been previously described by Page, et al. Briefly, genotyping was performed using a Transfusion Medicine microarray consisting of 879,000 SNPs; the data are available in dbGAP accession number phs001955.vl.pl. Imputation was performed using 811,782 SNPs that passed quality control. After phasing using Shape-IT, imputation was performed using Impute2 with the 1000 Genomes Project phase 3 all-ancestry reference haplotypes. We used the R package SNPRelate to calculate principal components (PCs) of ancestry. We performed association analyses for kynurenine using an additive SNP model in the R package ProbABEL and 13,091 study participants who had both metabolomics data and imputation data on serialsamples from stored RBC components that passed respective quality control procedures. Adjustments were made for sex, age (continuous), frequency of blood donation in the last two years (continuous), blood donor center, and ten ancestry' PCs. Statistical significance was determined using a p-value threshold of 5x1 O’8. Only considered variants with a minimum minor allele frequency of 1% and a minimum imputation quality score of 0.80. The OASIS: Omics Analysis, Search & Information a TOPMED funded resources, was used to annotate the top SNPS. OASIS annotation includes information on position, chromosome, alellele frequencies, closest gene, type of variant, position relative to closest gene model, if predicted to functionally consequential, tissues specific gene expression, and other information.

[0056] Mouse RBC storage and post-transfusion recovery Mouse storage and posttransfusion recovery (PTR) studies were performed as previously described, leveraging a population of 350 diversity' outbred mice (JAX DO) derived from extensive breeding of eight founder strains with genetically diverse backgrounds, as described. All animal procedures were approved by the University of Virginia IACUC (protocol no. 4269).

[0057] Determination of hemoglobin and bilirubin increment via the vein-to-vein database Association of metabolite levels with hemoglobin and bilirubin increment was performed by interrogating the vein-to-vein database, as described in Roubinian et al. Bucketing of metabolite levels in quartiles and tertiles for hemoglobin and bilirubin increments was informed by the significantly smaller record of entries for the latter parameter in the database.

[0058] Data analysis Statistical analyses - including hierarchical clustering analysis (HCA), linear discriminant analysis (LDA), uniform Manifold Approximation and Projection (uMAP), correlation analyses and Lasso regression were performed using both MetaboAnalyst 5.0 and inhouse developed code in R (4.2.3 2023-03-15).Example 2 - Donor sex, age, ethnicity' and BMI. as well as latent variables impact RBC metabolism of stored RBCs

[0059] Leukocyte-filtered RBCs were donated by 13,091 volunteers across four different blood centers in the United States and stored for 42 days (end of the shelf-life) prior to testing via high-throughput metabolomics (Figure 1A). Demographics were available for 13,029 of these donors. The donor population was balanced with respect to sex (6507 males and 6522 females), blood center (from a minimum of 3019 to a maximum of 3564 per center). The age range spanned from 18 to 87 (median age 47). Body mass index (BMI) ranged from 10-78 (median 26.6). Ethnicity was biased heavily towards Caucasians (7037), with good representation of individuals of Asian (1602), African (1542) and Hispanic descent (1153). Additive solution 1 and 3 (AS-3) were the two only additives used in this study. Consistent with prior publications on two-three log smaller cohorts. Linear discriminant analysis of all thesamples as a function of donor age, sex, body mass index (BMI) and ethnicity confirmed a strong impact of all these factors on the metabolic heterogeneity of end of storage units (Figure 1B-E). K mean clustering was then used to identity' latent factors influencing clustering of end of storage units upon unsupervised uniform Manifold Approximation and Projection (uMAP - Figure IF). Network view analysis of metabolites across the whole population revealed a central core of metabolites involved in glycolysis, the only energy generating pathway in mature RBCs (Figure 1G). Metabolite-metabolite correlations (Spearman) were thus used to map independent networks for each one of the 10 clusters derived from the K means analysis (Figure 1H). Results indicated a clear rewiring of RBC metabolism in samples from clusters 3 and 4. Hierarchical clustering analysis of samples based on the top 50 significant metabolites that discriminate the 10 clusters by ANOVA confirmed a strong signal separating donors in cluster 4 from the rest of the donors in this study (Figure II). Blood units from donors in this group were characterized by the highest glycolytic intermediates and products (pathway overview in Figure 2A), including adenosine triphosphate (ATP).Example 3 - Donors in cluster 4 are characterized by elevation in glycolysis

[0060] Elevated day 42 adenosine triphosphate (ATP) was observed in pRBCs from donors in cluster 4 (Figure 2B), despite overall decreases of ATP as a function of storage duration (e.g.. in 643 index donors donated a second unit of blood that was sampled at storage day 10, 23 and 42 - Figure 2C). Packed RBCs in cluster 4-5 were mostly stored in AS-3, while clusters 3, 6-8 mostly included blood units in AS-1 (Figure 8). ATP levels were non-normally distributed across the whole population (Figure 9), and were positively associated with all glycolytic metabolites, and negatively associated with hypoxanthine, an ATP catabolite. This conclusion was informed by hierarchical clustering analyses based on ATP levels (5thvs 95thpercentile - Figure 2D; extended analysis in Figures 10), and correlation analyses (Spearman - Figure 2E). Elevated hypoxanthine (Figure 11) or combination of high ATP, low hypoxanthine vs low ATP, high hypoxanthine (Figure 12) further confirmed these observations. Consistently, ridgeline plots for all glycolytic intermediates show elevation of hexoses, hexose phosphate and triose phosphates all the way to pyruvate in donors from cluster 4 (Figure 2F). Color-coding of the 3D uMAP based on ATP (Figure 2G) or hypoxanthine levels (Figure 9) showed that cluster 4 samples had the highest levels of ATP and lowest levels of hypoxanthine, throughout the whole age range of the donors in each one of the clusters (Figure 2H).Example 4 - Glycolysis is impacted by donor sex, age, ethnicity and BMI

[0061] Given the centrality of glycolysis in the unsupervised clustering of the 13,091 end of storage packed RBC samples, a breakdown of the donors based on demographics was performed to determine the impact on this pathway of factors like age, alone or in combination with sex.BMI and ethnicity (Figure 3), Results clearly show a progressive decline in the levels of ATP and all glycolytic intermediates, but increases in lactate as a function of donor age in all day 42 packed RBC samples (Figure 3A). This phenomenon showed sex dimorphism, with male vs female plots for 2,3 bisphosphoglycerate, phosphoenolpyruvate and lactate showed higher levels of glycolytic metabolites, but not ATP, and lower levels of glucose in RBCs from male donors - suggestive of higher glycolysis in males to fuel the Rapoport-Luebering shunt without net advantage on the generation of larger RBC ATP pools (Figure 3B). Of note, sex dimorphism in glucose consumption was canceled past year 50, the average national age for menopause in the United States.

[0062] RBCs from donors with the highest BMIs (morbidly obese; >40) were characterized by significantly higher levels of lactate compared to all the other BMI groups within the age window of 30-60 (Figure 3C).

[0063] Ethnicity showed a measurable impact on glycolysis, with end of storage RBCs from donors with Hispanic and other ethnicity being characterized by higher levels ATP and 2,3-DPG, and of all glycolytic intermediates through pyruvate, but not lactate - which instead was highest in donors of Caucasian or East Asian descent (Figure 3D).Example 5 - Genetic underpinnings of glycolytic heterogeneity in end of storage units from 13,091 REDS RBC Omics Index donors

[0064] In light of the observed impact of ethnicity on ATP and glycolysis in the REDS RBC Omics Index donor population, it was decided to delve into the genetic underpinnings of the heterogeneity in end of storage levels of these metabolites. Indeed, along with metabolomics analyses, as part of the REDS RBC Omics study donors were tested for -879,000 single nucleotide polymorphisms (SNPs - Figure 4A). A summary Manhattan plot of metabolite Quantitative Trait Loci (mQTL) is shown in B for all glycolytic metabolites merged (Figure 4B; Figure 13), or just for ATP (Figure 4C; Figure 14). Manhattan plot for ATP, lactate, as well as for all the other glycolytic metabolites are shown in Figure 4B-C and E, and Figure 5, respectively. Related Locus Zoom plots for the most common SNPs on phosphofructokinase P (PFKP, chromosome 10) are show n in Figure 4D and F. Of note, SNPs on PFKP also ranked as the strongest factor influencing hypoxanthine levels, followed by SNPs on the region coding for adenosine deaminase (ADA. chromosome 12 - Figure 4G). Significance of these analyses was confirmed by QQQ plot analysis (Figure 4H for hypoxanthine - downstream to ATP breakdown and deamination - Figure 41) - some of the hits ranking with FDR-corrected p-value in the order of e-163 (top ten list in Figure 4J).

[0065] This analysis also identified multiple common hot spot areas in the regions coding for other ratelimiting enzymes of glycolysis, hexokinase 1 (HKL chromosome 10),phosphoglucomutase 2 like 1 (PGM2L1, chromosome 11), pyruvate kinase (PKLR, chromosome 1 - Figure 5). Similarly, end of storage levels of pyruvate showed a strong association with the rate-limiting enzyme of the pentose phosphate pathway, glucose 6-phosphate dehydrogenase (G6PD - Figure 5). For glycolytic metabolites downstream to glyceraldehyde 3-phosphate, a common hit absent in the hexose phosphate group was the ectonucleotidase CD38 (BST1, chromosome 4 - Figure 5). For glyceraldehyde 3phosphate and ribose phosphate, a hit on RHCE (chromosome 1) suggests a potential cross-talk between the RH group components and the metabolic steps linking the non-oxidative branch of the pentose phosphate pathway to triose phosphate compounds (Figure 5). HSB1L-MYB (chromosome 6) and CPPED1 (chromosome 16) suggest a cross-talk between ATP and phosphoglycerate level and hemoglobin variants, including hemoglobin F and alpha-thalassemia in ATR-16 syndrome, deletions of -177 to -2000 kb affecting one allele of the w ell-characterized, gene dense, distal region of chromosome 16 (16pl3.3).

[0066] Of all the SNPs mentioned above, all glycolytic metabolites, ATP and hypoxanthine showed association with PFKP (Locus Zoom plots in Figure 15). Correlation analysis of PFKP SNP dosage across donors confirms a strong positive association with lactate (Figure 6A) and ATP levels (Figure 6B) and negative association with hypoxanthine (Figure 6C). SNP dosage was associated with increases in all glycolytic intermediates and increased consumption of glucose (Figure 6D).Example 6 - RBCs from donors with high end of storage ATP and low hypoxanthine are characterized by lower hemolysis in vitro and in vivo in autologous healthy and non-autologous critically ill recipients requiring transfusion

[0067] End of storage spontaneous hemolysis - one of the two FDA gold standard for the quality of stored blood products - was measured in all 13,091 units from the REDS RBC Omics index donor cohort. Lactate levels at storage day 42 were the top predictor of spontaneous hemolysis (Figure 7A). In a separate cohort of 79 volunteer from the Donor Iron Deficiency Study (DIDS) in frequent donors, glycolytic metabolites and ATP levels were positively associated to, and hypoxanthine negatively associated to measurements of post-transfusion recovery with 51Cr in healthy autologous volunteers - the second and final FDA gold standard to monitor the quality of stored RBCs (Figure 7B). By accessing the "vein-to-vein" database of the REDS RBC Omics program, end of storage ATP and hypoxanthine levels were linked to hemoglobin increments (n=5,816 records available in the database) and bilirubin increments (n=914) upon heterologous transfusion of products from the same donors to critically ill recipients requiring transfusion (Figures 7C-D). Results indicate a significant positive association between ATP levels and hemoglobin increments in transfusion recipients, withdecreased bilirubinemia (lower in vivo hemolysis) in patients receiving unis from donors with high end of storage ATP. Vice versa, opposite results were observed for hypoxanthine, a negative regulator of in vivo hemoglobin increments and elevated bilirubin increments.

[0068] While multiple embodiments are disclosed, still other embodiments of the present invention will become apparent to those skilled in the art from the following detailed description. As will be apparent, the invention is capable of modifications in various obvious aspects, all without departing from the spirit and scope of the present invention. Accordingly, the detailed description is to be regarded as illustrative in nature and not restrictive.

[0069] All references disclosed herein, whether patent or non-patent, are hereby incorporated by reference as if each was included at its citation, in its entirety. In case of conflict between reference and specification, the present specification, including definitions, will control.

[0070] Although the present disclosure has been described with a certain degree of particularity, it is understood the disclosure has been made by way of example, and changes in detail or structure may be made without departing from the spirit of the disclosure as defined in the appended claims.

Claims

CLAIMS1. A method of selecting an erythrocyte with enhanced stress-resistance characteristics, the method comprising: assaying the erythrocyte to determine a first biomarker level; comparing the first biomarker level of the ery throcyte to the first biomarker level in a plurality of comparator erythrocytes; selecting the erythrocyte if the first biomarker is ATP and the level is greater than the first biomarker level in 90% of the comparator ery throcytes.

2. The method of claim 1. further comprising assaying the erythrocyte to determine a second biomarker level, wherein the second biomarker is selected from hypoxanthine and lactate.

3. The method of claim 2. wherein the second biomarker is lactate and the erythrocyte is selected if the lactate level is greater than the lactate level in 90% of the comparator erythrocytes.

4. The method of claim 2, wherein the second biomarker is hypoxanthine and the erythrocyte is selected if the hypoxanthine level is less than the hypoxanthine level in 10% of the comparator erythrocytes.

5. The method of claim 3, comprising assaying the ery throcyte to determine a third biomarker level, wherein a third biomarker assayed and the third biomarker is hypoxanthine and the erythrocyte is selected if the hypoxanthine level is less than the hypoxanthine level in 10% of the comparator ery throcytes.

6. The method of claim 4. comprising assaying the erythrocyte to determine a third biomarker level, wherein the second biomarker is lactate and the erythrocyte is selected if the lactate level is greater than the lactate level in 90% of the comparator ery throcytes.

7. The method any one of claims 1-6, wherein the enhanced characteristic is selected from decreased hemolytic propensity, stability' in storage, post-perfusion resistance to hemolysis, stress tolerance, oxygen capacity, energy' metabolism.

8. The method of any one of claims 1 to 7, wherein the erythrocyte is obtained from a unit of donated blood and the donated unit of blood is administered to a patient in need thereof if the erythrocyte is selected.

9. The method of any one of claims 1 to 8, wherein the erythrocyte is genetically modified after selection.

10. A method of selecting a subject for blood donation comprising: obtaining genetic material from the subject; assessing the genetic material for the presence of at least one single nucleotide polymorphism selected from rs2388595, rs2388594, rs56882221, rs34538474, rsl0903962, rs872398, rs7914782, rsl2249060, rsl l592619, rs55845900, rs597808, rs2072931. rs9438900, rs609320, rs586178, rs9891699, rs2542591, rs2542590. rs2542573. rs2692513, rs2692512, rs4791641, rsl059476, rsl 1078738, rs805385, rs2241909, rs3826543, rs2949815, rsl050828, rsl984564, rs3764031, rs73264607, rsl74568, rsl74537, rsl02274, rsl74561, rsl6933078, rsl2210538, and rs8052118; and selecting the subject if the genetic material includes one or more SNPs selected from rs2388595. rs2388594. rs56882221, rs34538474, rsl0903962, rs872398, rs7914782. rsl2249060, rsl l592619, rs55845900, rs597808, rs2072931, rs9438900, rs609320, and rs586178.

11. The method of claim 10, further comprising selecting if the genetic material does not include one or more SNPs selected from rs9891699, rs2542591, rs2542590, rs2542573, rs2692513, rs2692512, rs4791641, rsl059476, rsl 1078738, rs805385, rs2241909, rs3826543, rs2949815, rsl050828, rsl984564. rs3764031, rs73264607, rsl74568, rsl74537, rsl02274, rsl74561, rsl6933078. rsl2210538, and rs8052118.

12. The method of claim 10 or claim 11, wherein the genetic material is electronic data.

13. The method of any one of claims 10-12, wherein the genetic material is deoxynucleic acid.

14. An array for determining erythrocyte characteritics, comprising: two or more oligonucleotides selected from rs2388595, rs2388594, rs56882221, rs34538474, rsl0903962, rs872398, rs7914782, rsl2249060, rsl 1592619, rs55845900, rs597808, rs2072931, rs9438900, rs609320, rs586178, rs9891699, rs2542591, rs2542590, rs2542573, rs2692513, rs2692512, rs4791641, rsl059476, rsl 1078738, rs805385, rs2241909, rs3826543, rs2949815. rsl050828. rsl984564, rs3764031, rs73264607. rsl74568, rsl74537, rsl02274, rsl74561, rsl6933078, rsl2210538, and rs8052118, or oligonucleotides that hybridize to same.

15. The array of claim 14, including oligonucleotides rs2388595, rs2388594, rs56882221, rs34538474, rsl0903962, rs872398, rs7914782, rsl2249060, rsl 1592619, rs55845900, rs597808, rs2072931, rs9438900, rs609320, and rs586178, or oligonucleotides that hybridize to same.1 . The array of claim 14 or claim 15, wherein the array consists of oligonucleotides rs2388595, rs2388594, rs56882221, rs34538474, rsl0903962, rs872398, rs7914782, rsl2249060, rsl l592619, rs55845900, rs597808, rs2072931, rs9438900, rs609320. rs586178, rs9891699, rs2542591. rs2542590. rs2542573, rs2692513, rs2692512, rs4791641, rsl059476, rsl 1078738, rs805385, rs2241909, rs3826543, rs2949815, rsl050828, rsl984564, rs3764031, rs73264607, rsl74568, rsl74537, rsl02274, rsl74561, rsl6933078, rsl2210538, and rs8052118, or oligonucleotides that hybridize to same.

17. A method for identifying an erythrocyte population with enhanced stress tolerance, comprising: obtaining a biological sample from the population, wherein the population is derived from a single first donor; identifying one or more biomarkers in the sample; determining a level for the one or more biomarkers; comparing the level of the one or more biomarkers in the sample to levels of the one or more markers in samples from a plurality of second donor, wherein the first donor's biomarker levels are higher or lower than 90% of the plurality of second donors.

18. A method for identifying a subject with enhanced stress tolerance, comprising: obtaining a biological sample from the subject; isolating genomic nucleic acids from the sample; contacting the nucleic acids with an array; analyzing the array for the presence of hybridized SNPs selected from rs2388595, rs2388594, rs56882221, rs34538474, rsl0903962, rs872398, rs7914782, rsl2249060, rsl 1592619, rs55845900, rs597808, rs2072931, rs9438900, rs609320, rs586178, rs9891699, rs2542591. rs2542590. rs2542573. rs2692513, rs2692512, rs4791641, rs!059476, rsl 1078738, rs805385, rs2241909, rs3826543, rs2949815, rsl050828, rsl984564, rs3764031, rs73264607, rsl74568, rsl74537, rsl02274, rsl74561, rsl6933078, rsl2210538, and rs8052118.

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