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Screening hydrogels for antifibrotic properties by implanting cellularly barcoded alginates in mice and a non-human primate

Abstract

Screening implantable biomaterials for antifibrotic properties is constrained by the need for in vivo testing. Here we show that the throughput of in vivo screening can be increased by cellularly barcoding a chemically modified combinatorial library of hydrogel formulations. The method involves the implantation of a mixture of alginate formulations, each barcoded with human umbilical vein endothelial cells from different donors, and the association of the identity and performance of each formulation by genotyping single nucleotide polymorphisms of the cells via next-generation sequencing. We used the method to screen 20 alginate formulations in a single mouse and 100 alginate formulations in a single non-human primate, and identified three lead hydrogel formulations with antifibrotic properties. Encapsulating human islets with one of the formulations led to long-term glycaemic control in a mouse model of diabetes, and coating medical-grade catheters with the other two formulations prevented fibrotic overgrowth. High-throughput screening of barcoded biomaterials in vivo may help identify formulations that enhance the long-term performance of medical devices and of biomaterial-encapsulated therapeutic cells.

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Fig. 1: Cell barcoding strategy enables high-throughput materials screening.
Fig. 2: High-throughput screening of combinatorially synthesized chemically modified alginates using unique cellular barcoding facilitates identifying new hydrogels with reduced fibrosis in immune-competent mice.
Fig. 3: Dual-donors barcoding enables scaling up of materials screening in the NHP model.
Fig. 4: Lead hydrogels show low fibrosis intraperitoneally in C57BL/6J mice.
Fig. 5: Lead hydrogel encapsulating xenogeneic human islets demonstrates a diabetic reversal in immunocompetent C57BL/6J mice.
Fig. 6: Lead small molecules can be translated for medical applications, including catheter coating.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw sequencing data generated in this study can be accessed via Figshare at https://figshare.com/projects/In_vivo_screening_of_hydrogel_library_using_cellular_barcoding_identifies_biomaterials_that_mitigate_host_immune_responses_and_fibrosis/144375. The datasets generated and analysed during the study are available for research purposes from the corresponding authors on reasonable request.

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Acknowledgements

This work was supported by the US National Institutes of Health (R01 DK120459 to O.V. and D.Y.Z), JDRF (3-SRA-2021-1023-S-B to O.V.), and the Rice University Academy Fellowship (to M.I.J.). We thank the support of the National Science Foundation for access to the ToF-SIMS, supported through CBET1626418. ToF-SIMS analyses and spin coating were carried out with support provided by the Shared Equipment Authority at Rice University. We also thank Rice University animal resource facility staff for their assistance with animal research.

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S.M., B.K., L.Y.C., D.Y.Z. and O.V. designed the studies, analysed data and wrote the paper. S.M., B.K., L.Y.C., M.D.D., J.L., A.H., L.L., M.I.J., P.D.R., S.G., I.J., D.I., T.R., T.T., C.F., P.S., J.O., O.V. and D.Y.Z. conducted the experiments. S.M., B.K., M.D.D. and L.Y.C. carried out the statistical analyses and prepared displays communicating datasets. R.N.M., T.T., D.Y.Z. and O.V. provided advice and technical support throughout, and D.Y.Z. and O.V. supevised the study. All authors discussed the results and the preparation of the paper.

Corresponding authors

Correspondence to David Yu Zhang or Omid Veiseh.

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Competing interests

O.V. is cofounder and significant shareholder of Sigilon Therapeutics, Avenge Bio and Pana Bio. D.Y.Z. owns significant equity in and receives consulting income from NuProbe Global and Torus Biosystems, and owns significant equity in Pana Bio. J.O. is a founding scientist and significant shareholder of Sigilon Therapeutics and CellTrans Inc.

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Extended data

Extended Data Fig. 1 Schematic diagram for the synthesis of 211 alginate analogs.

Left of the scheme are two of the previous triazole-containing lead alginates (B1-A21 and Z1-A34) that prevent fibrosis in mouse and NHP models. A total of 211 new alginate analogs were synthesized by varying the lead hydrophilic linker (Azido PEG-amine) and hydrophobic linker (iodobenzylamine) combined with a set of alkyne classes highlighted in blue colors.

Extended Data Fig. 2 Capsule optimization.

a, Gelation assay of alginate analogs using Rhodamine B entrapment. b, Representative images of capsule formation using the alginate analogs. Scale bar, 2 mm. c, After initial characterization studies, including purity, solubility, and gel-forming ability, 149 alginate polymers were used for the screening test. d, Mechanical testing (work to burst) with different alginate formulations. One-way ANOVA with Bonferroni correction was used for statistical analysis; ***P = 0.0007 (Z1-A34), ***P = 0.0009 (B1-A51), ns; non significant for comparisons with SLG20. All error bars denote mean ± s.e.m of n = 20 replicates. e-i, FITC-dextran permeability test with different modified alginates. All error bars denote mean ± s.e.m of n = 5 replicates.

Extended Data Fig. 3 Optimization of NGS library preparation workflow.

The library preparation involved a multiplex PCR step in amplifying the SNP loci, a barcoding PCR step to add position barcode to each sample, and a ligation-based sequencing adapter amendment procedure. a, Before optimization, the library on-target rate was <10%, with primer-dimers and non-specific PCR products contributing to the majority of reads. b, After optimization, the on-target rate was increased to >80% regardless of low DNA input (<1 ng) in the starting material.

Extended Data Fig. 4 Bioinformatic pipeline for determining material identity/composition from NGS sequencing data.

Fastq NGS data was demultiplexed by row and column barcodes to re-group sequences amplified from the same DNA input. Then for each amplicon sequence, the grep function was applied to search the dominant and variant alleles to calculate variant allele frequency (VAF) for each SNP locus. If the encapsulated cells comprised only one donor, the VAF profile was compared against profiles of the 20 pre-screened HUVEC donors. The donor with the highest match rate was identified as the encapsulated donor cell. When one or two donors were used as encapsulated cells, the log-likelihood of all possible donor compositions was calculated. The composition with the highest overall log-likelihood was determined as the cell composition (Quality control for log-likelihood analysis: 1) at least 25/30 SNP loci had sequencing coverage >50; and 2) overall log-likelihood higher than -200, and 3) goodness measurement higher than 10 where goodness is defined as the difference of log-likelihood between the most likely and the second most likely donor pairs). The material corresponding to the identified donor cell or cell composition would be the material encapsulating cells.

Extended Data Fig. 5 Dual donor barcoding identification in C57BL/6J mice.

a, Three different materials were tested; UP-VLVG (control), B1-A51 (one of the negative materials), and Z1-A34 (one of the positive materials). b, Schematic workflow of three materials screening containing mixed dual donors. c-d, After two weeks of implant, capsules were retrieved from each mouse (M1-M3) and were separated into three groups depending on fibrosis levels. e, Representative heatmap result of identified donor pair. f, 39 mapped to Z1-A34 (positive control material), coded by H16:H14 at 1:2 ratio; 4 mapped to UP-VLVG coded by H6:H8 at 1:2 ratio; 0 sample mapped to B1-A51. Overalled 43/45 samples were mapped from the 400 donor SNP profile. Proportions of each material corresponded to donor pairs were plotted, and Z1-A34 showed the highest value indicating the best immune-protective properties.

Extended Data Fig. 6 Diabetic reversal study with lead material.

a, Capsules containing human islets were fabricated at final cell density with 4,000, 8,000, and 16,000 IEQ/alginate volume (mL). The final IEQ values in each capsule were 10, 20, and 40 IEQ per capsule, respectively. In each group, 500 µL, 250 µL, and 125 µL of capsules were implanted in IP space, containing total 2,000 IEQ per mouse. All error bars denote mean ± s.e.m of n = 20 biological replicates. b, Representative images of pre-implant Z1-A34 capsules. Dithizone staining indicates viable islets within the capsule matrix. After encapsulation, islets show good viability (live: green, dead: red).

Extended Data Fig. 7 Material characterization and evaluation of catheters coated with lead molecules.

a-b, The total intensity of two main peaks analyzed by ToF-SIMS (a, CN and b, Br) were plotted to compare with the unmodified catheter. c, XPS data for unmodified, Met-Z1A3, and Met-B2-A17 modified catheters showing wt. % of small molecule-specific atoms, indicating successful coating. d, Representative SEM images of unmodified and coated catheters. e-g, Surface characteristic of catheters coated with lead molecules using Raman spectroscopy. Pre-implant or post-explant catheters (4 or 8 weeks) were analyzed to confirm the presence of coated molecules (e, Met-Z1-A3, f, Met-B2-A17, g, Met-Z1-A34 catheters). All error bars denote mean ± s.e.m of n = 2 replicates.

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Mukherjee, S., Kim, B., Cheng, L.Y. et al. Screening hydrogels for antifibrotic properties by implanting cellularly barcoded alginates in mice and a non-human primate. Nat. Biomed. Eng (2023). https://doi.org/10.1038/s41551-023-01016-2

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