Cas9-Mediated Knockout of Ndrg2 Enhances the Regenerative Potential of Dendritic Cells for Wound Healing

doi:10.21203/rs.3.rs-1781479/v1

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Cas9-Mediated Knockout of Ndrg2 Enhances the Regenerative Potential of Dendritic Cells for Wound Healing

https://doi.org/10.21203/rs.3.rs-1781479/v1

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posted 05 Jul, 2022

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Chronic wounds impose a significant healthcare burden to a broad patient population. Cell based therapies, while having shown benefits for the treatment of chronic wounds, have not achieved widespread adoption into clinical practice. Here, we developed a novel CRISPR/Cas9 approach to precisely edit dendritic cells (DCs) to enhance their therapeutic potential for healing chronic wounds. Using single-cell RNA sequencing (scRNA-seq) of tolerogenic DCs, we discover N-myc downregulated gene 2 (Ndrg2), which marks a specific population of DC progenitors, as a promising target for CRISPR knockout (KO). Ndrg2-KO alters the transcriptomic profile of DCs and preserves an immature cell state with a strong, pro-angiogenic and regenerative capacity. We then incorporated our CRISPR-based cell engineering within a hydrogel technology for in vivo cell delivery and developed a highly effective translational approach for DC based immunotherapy that accelerated healing of full-thickness wounds in both non-diabetic and diabetic mouse models. These findings could open the door to future clinical trials using safe gene editing in DCs for treating various types of chronic wounds.

More than 6.5 million patients suffer from chronic wounds in the United States, which impact nearly 15% of Medicare beneficiaries at an annual cost estimated at $28–32 billion.^1,2 Chronic wounds constitute a significant burden to the healthcare system as a whole and on the individual level, causing a profoundly negative psychosocial impact on affected patients. Treatment approaches like skin grafting or tissue engineered skin substitutes are helpful in clinical practice, but often require surgical intervention and frequently fail in the setting of complex wounds in patients with diabetes and peripheral vascular disease (PVD).³ These conditions lead to a dysfunctional healing response, characterized by an impaired ability to regenerate a functional microvasculature through the process of angiogenesis.⁴

For decades, cell-based therapies have been under development and demonstrated secretory, immunomodulatory, and regenerative effects on chronic wounds.⁵ However, unlike in oncology, treatment of chronic wounds with the body’s own cells has not been successfully translated into clinical practice, let alone become the standard of care for complex wounds. The “workhorses” of current cell therapy approaches in regenerative medicine are mesenchymal stromal cells (MSCs), which, despite their beneficial effect on wound healing, have a number of disadvantages. Isolation of MSCs requires tissue biopsies or surgical interventions such as liposuction,⁶ which are not indicated in the vast majority of patients presenting to wound clinics; moreover, stem cell cultivation and expansion comes at significant costs.^5,7

Dendritic cells (DCs) are hematopoietic cells that play critical roles in regulating both innate and adaptive immune responses. Depending on their activation status, DCs can promote peripheral immune tolerance (tolerogenic DCs), thus limiting the activation of the immune system and tissue damage.^8,9 For clinical applications, DCs can be easily isolated from the peripheral blood in large quantities using established good manufacturing practice (GMP) protocols for leukaphereses.¹⁰ Multiple clinical trials have reported beneficial effects of tolerogenic DC therapy in the treatment of autoimmune diseases, such as multiple sclerosis, rheumatoid arthritis, type 1 diabetes, and Crohn’s disease, as well as transplant rejection.^11–17 In addition, Sipuleucil-T, a DC vaccine, has demonstrated survival benefits in patients with castration-resistant prostate cancer,¹⁸ leading to its approval by the US Food and Drug Administration (FDA) in 2010.^18,19 Despite these major clinical advances of DC immunotherapy, the application of DCs for wound healing has not been investigated.

CRISPR/Cas9 gene editing has revolutionized the development of T cell therapeutics for enhanced anti-cancer immunity in hematologic and solid tumors, demonstrating benefits in multiple clinical trials.^20–23 In addition, gene editing has been shown to improve the efficacy of MSCs for wound healing.²⁴ Whether CRISPR/Cas9 technology can modulate and enhance the therapeutic effect of DC immunotherapy has not been investigated so far. Here, we used a novel CRISPR/Cas9 based approach to enhance the therapeutic potential of DCs in order to develop an effective and translatable cell-based therapy for chronic wounds. Using single-cell RNA sequencing (scRNA-seq) of tolerogenic DCs, we discovered N-myc downregulated gene 2 (Ndrg2), which marks a specific population of DC progenitors, as a promising target for CRISPR-knockout (KO). KO of Ndrg2 alters the transcriptomic profile of DCs and preserves an immature cell state with a strong pro-angiogenic and regenerative capacity. We further combinedincorporated our in vitro CRISPR-based cell engineering withinto a hydrogel technology for in vivo cell delivery and developed a highly effective translational approach for DC therapy of chronic wounds, thereby opening a door for future clinical trials using safe gene editing of DCs as regenerative cell therapies.

Ndrg2 expression is reduced in tolerogenic DCs

To identify potential targets for gene editing in DCs, we compared transcriptomic profiles of tolerogenic DCs, which confer a variety of clinical benefits,^11-17 with untreated DCs. Bone marrow-derived DCs were cultivated from wild-type (WT) mice (C57/BL6) according to standard protocols. Vitamin D3 (calcitriol, VD3) was used to induce tolerogenic DCs, and scRNA-seq of VD3 stimulated and untreated DCs was performed (Fig 1a, b). Nine transcriptionally distinct clusters resembling pre-mature (characterized by high expression of Cd34, Csf1r (encoding CD115), Ccr2, and Cx3cr1²⁵) and mature BM-DCs (characterized by high expression of Flt3 (encoding CD135)²⁶ and Ccr7^27,28) were identified (Fig 1b, Extended Data Fig 1a). We found that tolerogenic DCs induced by VD3 showed a downregulation of Ndrg2, which has previously been reported to inhibit growth factor expression, angiogenesis, and cell proliferation.^29-33 In accordance, tolerogenic DCs showed an overexpression of vascular endothelial growth factor (Vegfa), indicating pro-angiogenic properties potentially related to Ndrg2 inhibition. Thus, we identified Ndrg2 as an interesting target for further investigation (Fig 1c, Extended Data Fig 1b).

Ndrg2 marks DC progenitors

Among the untreated DCs, the strongest Ndrg2 expression was found in cluster 8 (C8, Fig 1d, Extended Data Fig 1d), which co-expressed the hematopoietic stem cell marker Cd34, and Csf1r (encoding CD115), as well as Flt3 (encoding CD135) and Clec9a (encoding DNGR-1). This expression profile is consistent with previously defined myeloid progenitor populations, namely MDP (macrophage and dendritic cell progenitor) and CDP (common dendritic cell progenitor), for which phenotypic overlap has been described.^34-36 Interestingly, Ndrg2+ progenitors also expressed Itage (CD103) while being negative for Cd8 (Fig 1e, Extended Data Fig 1d).

Compared to untreated cells, VD3 treatment almost completely abrogated the expression of Ndrg2 and its co-expressed genes Cd34, Flt3, Itgae, and Clec9a, while inducing Kit expression (Fig 1e, Extended Data Fig 1d). This was further confirmed with flow cytometry showing that 3% of all CD34+ DC progenitors in untreated cultures co-expressed Flt3 (CD135) and CD103, while this population was reduced to 0.8% after VD3 treatment (Fig 1f).

Ndrg2 inhibition in DCs promotes EC tube formation in co-cultures

To investigate whether inhibition of Ndrg2 could promote a pro-angiogenic behavior in DCs, we co-cultured BM-DCs with endothelial cells (ECs) and analyzed their angiogenic capacity in EC tube formation assays. Ndrg2 was inhibited according to established protocols using VD3 and lipopolysaccharide (LPS) or dexamethasone (Fig. 1g).²⁹ We found that Ndrg2-inhibited DCs stimulated EC tube formation in vitro, as indicated by a higher EC branch and junction number, longer branch length, and higher total mesh area compared to EC co-cultures with untreated DCs and ECs cultured in basal media (Fig. 1i, Extended Data Fig. 1e, f). The strongest angiogenic response was found in co-cultures with VD3-treated DCs (Fig. 1i, h). This was confirmed in a Luminex multiplex ELISA for 43 proteins performed on culture media of DC monocultures, which showed that DCs treated with VD3 had the highest VEGFA secretion compared to untreated DCs, DCs treated with LPS, or even DCs treated with VD3 and LPS (Fig. 1j).

Given the strong expression of Ndrg2 in DC progenitors and the pro-angiogenic response created by pharmacologic Ndrg2 inhibition, we hypothesized that KO of Ndrg2 would allow us to alter the transcriptional fate and development of BM-DCs, thus allowing us to generate cells that can favorably impact the wound healing process. Furthermore, precise KO of Ndrg2 would avoid pleiotropic effects of VD3, in which unpredictable off target or systemic effects could occur and would also precisely interrogate whether the observed pro-angiogenic features of tolerogenic DCs were in fact due to loss of Ndrg2 expression.

Development of a CRISPR/Cas9 platform for KO of Ndrg2 in primary DCs

To develop an approach for CRISPR/Cas9 KO of Ndrg2 in primary murine BM-DCs, we used nucleofection with ribonucleoprotein complexes assembled from Cas9 protein and sgRNA (Cas9-RNP; Fig. 2a,b). This technique for CRISPR/Cas9 gene editing has recently been demonstrated to be very effective for editing primary murine and human DCs^37,38 and has several advantages compared to previously employed lentiviral vector based approaches.³⁹ To achieve the highest editing efficiency, we compared indels and KO rates after Cas9-RNP nucleofection of DCs using two sets of three sgRNAs from different manufacturers targeting the Ndrg2 genomic locus as well as different numbers of cells per reaction and different RNP concentrations (Extended Data Fig. 2a, Table S1). After optimization, using GFP-fused Cas9 protein (dCas9-GFP) and flow cytometry, we confirmed a >90% transfection efficiency of DCs with our approach for nucleofection (Fig. 2c). Sanger Sequencing and the Interference of CRISPR Editing (ICE) tool (Synthego) were used to compute indel and KO rates, which were 91% resp. 88% using a triple-guide RNP approach (Fig. 2d, Extended Data Fig. 2a, Methods). We also performed immunocytochemical staining of CRISPR-edited and untreated DCs in vitro and observed a significant reduction in Ndrg2 protein expression (Fig. 2e).

Gene editing with Cas9-RNP showed no observable off-target effects

To analyze specific mutations introduced by CRISPR gene editing in DCs at the target site as well as any potential off-target effects, we performed whole genome sequencing (WGS) with an ultrahigh depth (~100X coverage) of CRISPR-edited DCs using our triple-guide RNP approach (Fig. 2f). Wild-type DCs were analyzed as controls. A variety of different on-target effects were identified, the most common of which were upstream gene variants, followed by 5’ UTR variants (Fig. 2g). Cas-OFFinder was used to identify potential off-target sites, which allows for unbiased extensive mutation searching. Importantly, indels were not detected within 15 base pairs (bp) up- and downstream of the sites predicted by Cas-OFFinder⁴⁰. When each site was broadened to 200bp up- and down-stream, indels in 14 sites were identified (Fig. 2h, Table S2). By manual inspection of these loci, we found that only 7 loci showed indels that occurred only in the Ndrg2-KO group and not in the control group. After identifying the most likely off-target sites, we further aligned the sequence between the predicted Cas-OFFinder cut sites and the three sgRNA sequences targeting Ndrg2. Among the three sites that showed the highest alignment, we only identified a protospacer adjacent motive (PAM) in two of the sites. All other predicted off-target sites did not show similarity to the sgRNA sequences and therefore are unlikely true off-target sites (Extended Data Fig. 2b, c). This set of analysis together confirmed that the designed guide RNAs and the use of RNP in DCs cells had little or no observable off-target effects.

Ndrg2-KO prevents DC maturation and induces regenerative gene expression profiles

To assess the impact of Ndrg2-KO on the transcriptional fate of DCs, we performed scRNA-seq of CRISPR-edited DCs and compared their transcriptomic profiles with those of untreated DCs and DCs treated with VD3, which pharmacologically inhibits Ndrg2 (Fig. 1a). A total of 23,871 cells were analyzed (Fig. 3a). We employed RNA velocity analysis using dynamical modeling with the scVelo package to determine changes in DC differentiation in response to Ndrg2-KO⁴¹. A differentiation stream was identified originating from the Ndrg2+ progenitors (cluster 9) that progressed through clusters 1 and 3 and ended in the mature DCs in cluster 0 (Fig. 3a, b). Most Ndrg2-KO cells were found in cluster 1, whereas control and VD3 treated cells mostly localized to cluster 0 (Fig. 3c). Using CytoTRACE, we compared the relative differentiation states of individual cells based on the distribution of unique mRNA transcripts and found that cluster 1, which mostly contained Ndrg2-KO cells, was among the least differentiated clusters, whereas cluster 0, which contained mostly VD3 treated and untreated cells, showed more advanced differentiation states (Fig. 3d)⁴². In accordance, KO of Ndrg2 led to a downregulation of the DC maturation markers Cd80 and Cd83 and an upregulation of Csf1r, which marks premature DCs (Fig. 3e). Hence, our findings indicate that Ndrg2 is highly expressed in DC progenitor cells and promotes DC differentiation toward mature phenotypes. In addition to a reduction of global Ndrg2 expression (Extended Data Fig. 3a), Ndrg2-KO using our Cas9-RNP approach almost eliminated cluster 9, which contained the Ndrg2+ progenitors and thereby halted DC maturation, preserving a pre-mature DC state (Fig. 3f). These engineered pre-mature DCs exhibited an even stronger Vegfa expression than DCs treated with VD3, and in addition exhibited an up-regulation of extracellular matrix associated genes that promote wound healing, such as Fn1 (encoding fibronectin-1) and Mmp12 (matrix metalloproteinase-12), as well as anti-oxidative genes Prdx4 and Mgst1 (Fig. 3g, Extended Data Fig. 3b). By contrast, control DCs highly expressed pro-inflammatory markers, such as S100a6 and S100a4 (Fig. 3h).³³

To investigate differential regulation of cellular signaling pathways between our conditions, we then used GeneTrail 3, a computational pipeline for over-representation analysis (ORA) of specific gene sets on a single-cell level, and found a significant upregulation of pathways related to angiogenesis, epithelial cell migration, and degradation of the ECM in Ndrg2-KO cells, indicating a beneficial impact on wound healing (Fig. 3i).⁴³

When analyzing the expression levels of genes located at the aforementioned potential off-target sites, Amy1, Ftsj1, Sema5a, and Minar1 (Table S2), no detectable expression among all groups was found for Sema5a and Minar1. Ftsj1 and Amy1 were expressed at very low levels in less than 6% resp. 0.06% of all cells without significant differences, confirming that our approach for gene editing is highly specific and does not cause significant off-target effects (Extended Data Fig. 3c).

Hydrogel delivery of Ndrg2-KO DCs promotes healing of non-diabetic wounds

Having developed and characterized our approach for gene editing to specifically induce regenerative gene expression profiles in DCs, we next aimed to test the suitability of these cells as a cell-based therapy for wound healing. The effectiveness and biocompatibility of hydrogels for cell delivery has been shown in murine and porcine wound models in combination with multiple cell types.^5,44-48 We seeded engineered DCs into a previously developed pullulan-collagen hydrogel to facilitate effective cell delivery onto the wound bed.^44,48 High viability and uniform distribution within the hydrogel were confirmed using calcein AM staining of live cells cultured on these hydrogels for 7 days (Fig. 3j).

To determine the therapeutic potential of Ndrg2-KO-DCs, we treated splinted full-thickness excisional wounds in wild-type mice (C57BL6) with hydrogels seeded with either control or Ndrg2-KO DCs. For controls, we subjected DCs to the same Cas9-RNP protocol, using 3 non-targeting sgRNAs to account for any potential effect of the Cas9-RNP or the nucleofection itself on wound healing. We also used a second control group of wounds treated with blank, unseeded hydrogels (Fig. 4a). Wounds treated with our engineered Ndrg2-KO DCs healed significantly faster and were completely re-epithelialized by day 11, 5 days faster than in either control group (Fig. 4b-d). Histologically, healed wounds treated with Ndrg2-KO-DCs showed a fully regenerated epithelium and a thicker dermis compared to wounds treated with control DCs or blank hydrogels (Fig. 4c-e). Immunofluorescent staining for CD31 revealed a significantly stronger vascularization of healed wound tissue that had been treated with engineered DCs compared to healed wound tissue from the control groups, confirming that the engineered DCs promoted angiogenesis and accelerated wound healing (Fig. 4f).

Hydrogel delivery of Ndrg2-KO DCs promotes healing of diabetic wounds

We then investigated whether our novel DC therapy would also be beneficial for chronic wounds with impaired healing potential, such as in diabetes mellitus. Therefore, we treated excisional wounds in db/db mice with either Ndrg2-KO-DCs, control DCs (nucleofection with 3 non-targeting sgRNAs), or blank hydrogels (n = 5 per group, Fig. 4g). The db/db mice have a homozygous mutation in the leptin receptor gene (Lepr), resulting in morbid obesity, chronic hyperglycemia, and pancreatic beta cell atrophy. It has been shown that excisional wounds in db/db mice show a significant delay in wound closure, decreased granulation tissue formation, severely impaired vascularization, and reduced cell proliferation compared to wounds in WT and other diabetic mouse strains.⁴⁹ In accordance with our findings in WT mice, Ndrg2-KO DCs significantly accelerated wound healing in db/db mice, leading to complete re-epithelialization by day 16, five days earlier than in the control groups (Fig. 4h, i, Extended Data Fig. 4a, b) and similar to what we had observed in WT mice. The engineered DCs also promoted angiogenesis, similar to the effects seen in the WT mouse experiments, significantly improving vascularization of diabetic wound tissue compared to control DCs and unseeded hydrogels (Fig. 4j).

Ndrg2-KO DCs target wound fibroblasts via growth factor signaling

We next aimed to further elucidate the molecular mechanisms underlying the accelerated wound healing and increased neovascularization in response to engineered DC therapy. To interrogate how the transplanted DCs interact with different cell types of the wound healing environment and affect their individual transcriptional signatures, we performed scRNA-seq of wound tissue treated with Ndrg2-KO-DCs, control DCs, or blank hydrogels (Fig. 5a). Tissue was explanted from a second batch of mice (n = 5 per group) on day 10 after wounding, when the wound healing curves of the different groups began to diverge (Fig. 4d), indicating critical differences in wound biology. We performed scRNA-seq on 10,612 cells across the three conditions, which were identified as fibroblasts, myeloid cells, neutrophils, lymphoid cells, and erythrocytes (Fig. 5b, c). Fibroblasts had the highest number of differentially expressed genes (DEGs, FC > 0.5, p< 0.05) between the groups among all cell types, indicating that our engineered DC therapy had the strongest impact on fibroblast gene expression within the wound bed (Fig. 5d, e). Among the most differentially expressed genes expressed by fibroblasts, we found several genes that have been shown to be critically involved in wound healing. Fibroblasts from wounds treated with Ndrg2-KO DCs almost exclusively expressed Ngfr, the receptor for nerve growth factor, which has been shown to promote cell proliferation, angiogenesis, and wound healing (Fig. 5f, g).⁵⁰ Moreover, treatment with Ndrg2-KO DCs strongly upregulated the expression of Lgals1 (encoding galectin-1) in fibroblasts, which promotes wound healing in diabetic and non-diabetic wounds.^51,52 Interestingly, we also found an upregulation of Plod2, encoding lysyl hydroxylase 2, which catalyzes the first step of collagen cross linking, and Ppib, encoding peptidyl prolyl cis/trans isomerase, which catalyzes the folding of ECM proteins such as collagen. These findings indicate that wound fibroblasts may be activated by Ndrg2-KO DCs to increase ECM protein synthesis and collagen cross linking in the proliferative phase of wound healing, leading to accelerated wound closure.

To study the intercellular communication networks between the transplanted DCs and the local cells of the wound, we integrated scRNA-seq data from Ndrg2-KO and control DCs (Fig. 3a) with transcriptomic data from the respective local wound environments (Fig. 5b) and used CellChat to analyze ligand-receptor interactions.⁵³ We found that Ndrg2-KO DCs showed a stronger outgoing signaling activity within their wound environment compared to control DCs (Fig. 5h) and also demonstrated a stronger total interaction strength within the aggregated cell-cell communication network (Fig. 5i). CellChat analysis demonstrated significant VEGF signaling from Ndrg2-KO DCs to fibroblasts, which was not present in control DCs, confirming our previous observations (Fig. 5j). In contrast to control DCs, Ndrg2-KO DCs also communicated with wound fibroblasts via other growth factor pathways, such as the insulin-like growth factor (IGF) and platelet-derived growth factor (PDGF) pathways (Fig. 5l, m), which stimulate cell proliferation and angiogenesis, and showed an upregulation of Spp1 (osteopontin) signaling, which has been shown to be a critical driver of early wound repair in diabetic wounds (Fig. 5m).^54-56

Here, we have expanded the use of DC immunotherapy to complex diabetic and non-diabetic wounds and developed an effective gene editing approach to boost its therapeutic efficacy. While stem cells have been heavily investigated in pre-clinical studies as cell-based therapies for the treatment of wounds, the translational advancements many have hoped for have not been made over the past decades.^45,46 By contrast, adoptive cell transfer using a patient’s own T cells or DCs has been successfully translated to the clinic and led to oncologic breakthroughs in recent years.⁵⁷ Combining gene editing with immunotherapy enabled the development of CAR T cell therapy, which demonstrated unsurpassed effectivity against hematologic malignancies and a potential cure for patients who are refractory to standard chemotherapies.⁵⁸ In addition to T cells, using a patient’s own DCs to treat solid tumors and multiple auto-immune conditions has demonstrated adequate safety profiles and strong therapeutic efficacy.^{11,13,15,17,18}

Gene editing of DCs for non-therapeutic use has previously been performed using lentiviral vector-based approaches. This technique, however, has several disadvantages for the development of clinical-grade DCs for cell-based therapies. Lentiviral transduction of DCs itself presents considerable challenges compared to other cell types. As professional antigen-presenting cells (APCs), DCs naturally express viral restriction factors, such as SAM domain and HD domain-containing protein 1 (SAMHD1), which allow them to deplete the cytoplasmic pool of deoxynucleoside triphosphates (dNTPs) necessary for the reverse transcription of viral genome.⁵⁹ Hence, previous studies have reported relatively low transduction efficiencies (< 40%) or required very high vector doses, which can induce undesired DC maturation or even cell toxicity.⁶⁰ Approaches to induce proteasomal degradation of SAMHD1 using Vpx or shRNA-mediated down-regulating of SAMHD1 to improve transduction efficiency have been described; however these techniques are not suitable for the development of clinical-grade DC therapies, since they increase the risk for cell infection with viruses and induce a cytotoxic T cell response.^61,62

Recently, Cas9 RNP-based gene editing of CAR T cells have been implicated for enhanced solid tumor killing.²² Furthermore, despite being early in the clinical test stage, Cas9 RNP-editing of murine primary DCs has been demonstrated to be an effective approach leading to high KO rates for several target genes, while preserving cell viability and in vitro functional characteristics.³⁸ Unlike vector-based approaches, Cas9 RNP-editing does not require the cellular transcription and translation of Cas9 to generate functional Cas9-sgRNA complexes, thus enabling higher peak Cas9 expression after transfection. In addition, rapid protein degradation of Cas9 from cells has been shown to increase CRISPR specificity and reduce the exposure of cells to Cas9, thus minimizing off-target effects.³⁹

We have developed a novel approach for KO of Ndrg2 in premature DCs using nucleofection with triple-guide Cas9-RNPs, which allows for streamlined, robust, and high-throughput editing of large quantities of DCs without the need for viral vectors. Our platform for gene editing consistently achieved > 90% KO rates in primary DCs. To our knowledge, our study is the first to comprehensively assess off-target effects after Cas9-RNP nucleofection of DCs via ultra-deep DNA sequencing, demonstrating a high specificity of our CRISPR approach without detectable off-target effects.

Previous studies reported that Ndrg2 is involved in DC maturation, however, its exact role has been poorly defined so far.²⁹ Using scRNA-seq of over 20,000 BM-DCs, we show that Ndrg2 marks MDP/CDPs and that its knockout prevents DC maturation, preserving an immature cell state that is characterized by strong regenerative and pro-angiogenic gene expression profiles that cannot be achieved by pharmacologic treatment with VD3. In addition, targeted alteration of cells by CRISPR/Cas9 gene editing has a higher specificity and avoids pleiotropic effects of pharmacologic cell treatment. Moreover, CRISPR-KO provides a durable effect on cells, since target genes are eliminated, which may be of particular importance for future translational applications when genetically edited cell products are stored, or clinical application is delayed.

DCs edited with our approach were suitable for seeding in delivery hydrogels for in vivo application as a cell-based therapy. The collagen-pullulan hydrogels used in our study provide an optimal environment similar to native ECM and have been demonstrated to be an ideal vehicle for efficient cell delivery.^44,48 In addition, these hydrogels, even without cells, have a positive impact on wound healing.^5,63 Wounds treated with control DCs did not heal faster compared to those treated with unseeded hydrogels, which indicates that DCs alone may not provide an additional benefit over collagen-pullulan hydrogels for wound healing. However, KO of Ndrg2 induced regenerative transcriptomic profiles in DCs, which led to significantly accelerated wound healing of WT as well as diabetic wounds by 5 days, surpassing what has previously been reported for genetically edited MSCs.²⁴

When analyzing the cellular ecology of the healing wounds in the proliferative phase of wound healing at day 10, we found that edited DCs specifically directed growth factor signaling (VEGF, PDGF, and IGF) toward fibroblasts in the wound bed. Fibroblasts are among the most critical cell types in wound healing and showed an upregulation of wound healing and ECM related genes after treatment with edited DCs, which indicates their strong therapeutic response and provides a molecular basis for the observed accelerated wound closure with edited DCs.

A recent study demonstrated high editing efficiencies of human DCs with Cas9 RNP nucleofection³⁷, which indicates that our platform for gene editing may be translated to human DCs. Since clinical-grade manufacturing of autologous DCs for adoptive cell transfer is already well established in clinical practice and DC immunotherapy is already being used in multiple clinical trials,^18,64 we believe that genetically edited DCs have a high potential for successful clinical translation as a cell-based therapy for patients suffering from complex wounds that are refractive to treatment with the current standard of care. In future clinical applications, DC harvest could easily be performed from standard blood samples obtained at wound clinics, without the need for tissue biopsies or other surgical interventions (Extended Data Fig. 4c).

In summary, our genetically edited DC therapy provides a highly effective, robust, and easy-to-use approach for the treatment of chronic wounds. It provides strong evidence towards and opens the door for future clinical trials of DC therapy combined with gene editing. These findings could provide significant translational opportunities to help patients with diabetic and non-diabetic ulcers.

Acknowledgments

We thank Yujin Park for her assistance in histopathology and Theresa Carlomagno for administrative support. Confocal imaging was performed at the Cell Sciences Imaging Facility, with generous support from the Beckman Foundation. Cell sorting/flow cytometry analysis for this project was done on instruments in the Stanford Shared FACS Facility. Single cell sequencing was supported by the Stanford Functional Genomics Facility (SFGF) with funds from the NIH (S10OD018220 and 1S10OD021763). This work used the Genome Sequencing Service Center (GSSC) by Stanford Center for Genomics and Personalized Medicine Sequencing Center, supported by the grant award NIH S10OD025212, and the Diabetes Genomics and Analysis Core of the Stanford Diabetes Research Center supported by grant award NIH/NIDDK P30DK116074.

L.S.Q. acknowledges support from Li Ka Shing Foundation, National Science Foundation CAREER award (Award # 2046650), Stanford Maternal & Child Health Research Institute (MCHRI), NIH Common Fund 4D Nucleome Program (grants U01 EB021240 and U01 DK127405), and California Institute for Regenerative Medicine (CIRM, DISC2-12669). The work is supported by Li Ka Shing Foundation gift fund (L.S.Q.), Stanford MCHRI (L.S.Q.).

Isolation and cultivation of BM-DCs

Cultivation of DCs from the murine bone marrow was performed according to previously published protocols by Lutz et al.⁶⁵ Briefly, after euthanasia, femurs and tibias were excised and the epiphyses on both ends of the long bones were removed. Bone marrow was collected by flushing the bones with 5 ml RPMI. Red blood cells lysis was performed with ACK lysis buffer. Cells were strained and then washed in complete media. Cells were cultured at a concentration of 2x10⁶ per 100 mm culture dish in 10 ml RPMI containing 10% fetal bovine serum (FBS), 100 U/ml penicillin, 0.1 mg/ml streptomycin, 2 mM glutamine, 50 μM 2-mercaptoethanol, and 200 murine granulocyte-macrophage colony-stimulating factor (GM-CSF, Peprotech, Frankfurt, Germany). On day 3, 10 ml complete media were added to the culture dishes and on day 6, 10 ml of media were collected, cells were spun down and re-suspended in fresh media. For all experiments, only cells in suspension that represent the DC fraction of the cultures were used, and adherent cells were discarded. For in vitro experiments, DCs were treated on day 7 of culture for 24 h with either 10^-6M 1,25 dihydroxy-cholecalciferol (Sigma-Aldrich, St. Louis, MO), 10^-6M 1,25 dihydroxy-cholecalciferol and 100 ng/ml LPS (Escherichia coli, 026:B6; ThermoFisher) or 10^-7 M dexamethasone (Sigma Aldrich).

Endothelial cell tube formation assay

Human umbilical vein endothelial cells (HUVECs) were obtained from Cell Applications (San Diego, CA). Tube formation assays were performed according to previously published protocols.⁶⁶ Briefly, HUVECs were seeded at 2 × 10⁵ cells per 75 cm² in T75 culture flasks and expanded in Human EC Growth Medium (Cell Applications) until 75% confluence was reached. After washing off growth media, cells were serum starved one day before the assay by incubation in Human EC Basal Medium (Cell Applications) for 24 h. Calcein AM (green; ThermoFisher) was added to cultures at a concentration of 2 µg/ml to stain cells and assess cell viability. Cells were incubated with Calcein AM in the dark at 37 °C and 5% CO₂ for 30 min, then the stain was washed off using PBS. Assays were performed in 24-well plates coated with Geltrex LDEV (lactose dehydrogenase elevating virus)-Free Reduced Growth Factor Basement Membrane Matrix (ThermoFisher) at 75 µl/cm² according to the manufacturer’s recommendations. Cells were plated at a concentration of 3.5 x 10⁴ in 200 µl Human EC Basal Medium (Cell Applications) per well. For co-culture assays, DCs were stained using CellTrace Calcein Red-Orange (ThermoFisher) and added to the EC cultures at a 1:1 ratio. Assays were run for 12 h and then fixed using 4% paraformaldehyde and imaged using a Zeiss LSM 880 confocal laser scanning microscope.

CRISPR/Cas9-mediated Ndrg2 knockout and efficiency analysis

Single-guide RNA (sgRNA) design was performed using design tools from Integrated DNA Technologies (IDT, Newark, NJ) and Synthego (Menlo Park, CA). All spacer sequences of Ndrg2 sgRNA, positive and negative control sgRNAs, primers for PCR amplification and DNA Sanger sequencing are listed in Table S1. RNPs were assembled using Cas9 Nuclease V3 purchased from IDT and directly synthesized together with Alt-R CRISPR-Cas9 sgRNAs from IDT or three guides as part of a multi-guide design from Synthego. For nucleofection we used the 4D-Nucleofector System and P3 Primary Cell 4D Nucleofector X Kit L (Lonza, Basel, Switzerland). We optimized the RNP electroporation for DCs by using multiple-guide mixtures and different number of cells in a 100uL total volume. The electroporated cells were plated in Costar Ultra-Low Attachmentat 6-well plates (Corning) at 0.5 million cells per well in 2 mL medium. The cells from one well were harvested after 48 hours and the total genomic DNA was extracted using Qiagen DNeasy Blood & Tissue kit. To analyze and verify KO efficiency, we amplified about 350bp genome region covering 175bp upstream and downstream of sgRNA targeting sites. The PCR products were column purified and then sequenced by Sanger Sequencing. The cutting and knockout efficiency were estimated using the ICE analysis tool (Synthego). After several runs of optimization, we used Cas9-RNP and the Lonza 4D-Nucleofector System together with the P4 Primary Cell 4D-Nucleofector X Kit L (Lonza) to transfect and create Ndrg2-KO DCs. To generate RNP, we used 18.6 pmol of Cas9 and 22 pmol of equimolar mixture of sgNDRG2_1, sgNDRG2_2 and sgNDRG2_3 (Synthego, Table S1, Extended Data Fig. 2a) per 0.5 million cells that were washed with DPBS and suspended in R Buffer (Lonza). After optimization, we processed 3 million DCs in each 100µL electroporation system for in vitro and in vivo experiments. All other steps followed IDT (Newark, NJ) Alt-CRISPR-Cas9 protocol. Cells were transfected using the program DK-100. We used a mixture of non-targeting sgRNAs (sgNC1, sgNC2, and sgNC3, Table S1) in the control RNP electroporation. For each cell production for in vivo experiments, we processed 30 millions of each Ndrg2-KO DCs and non-targeting control DCs.

Whole genome sequencing

Genomic DNA from Ndrg2-KO cells and control electroporated cells was extracted and whole genome sequencing (WGS) at ultrahigh depth (100X) was performed to confirm KO efficiency and evaluate off-target effects. WGS was performed by Novogene Corporation Inc. (Scaramento, CA). The genomic DNA was randomly fragmented by sonication to the size of 350 bp, then DNA fragments were end polished, A-tailed, and ligated with the full-length adapters of Illumina sequencing, and followed by further PCR amplification. The PCR products as the final construction of the libraries were purified with AMPure XP system. Then libraries were checked for size distribution by Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), and quantified by real-time PCR (to meet the criteria of 3 nM). Samples were sequenced using Illumina Novaseq 6000 on an S4 Flowcell. Sequencing was performed according to the manufacturer’s recommendations.

Off-target analysis

The resulting reads for both samples were then aligned to the mouse reference genome (GRCm38) using Burrows-Wheeler Aligner (BWA, Version 0.7.15) in the ‘bwa mem’ setting with default parameters.⁶⁷ To identify potential off-target sites, we followed an approach of previous studies using CRISPR-edited cells for clinical applications in human patients.²³ First, we conducted variant calling using the strelka2 ‘somatic workflow‘ setting for paired samples (Version 2.9.2).⁶⁸ All variants were then annotated using the Ensembl Variant Effect Predictor (Version 101, assembly="GRCm38.p6", dbSNP="150", gencode="GENCODE M25"). Additionally, we used the Cas-OFFinder webtool (Version 2.4) to predict potential off-target sites using all three sgRNA sequences.⁴⁰ Here, we searched for sequences with five or fewer mismatches and DNA and RNA bulge sizes of two or less. To identify variants that might be potential off-target sites, we only considered insertion or deletion events that were not detected in the control sample, that passed all quality control checks (‘FILTER = PASS’), and that did not contain the ‘SNVHPOL’ information in their VCF ‘INFO’ field. Additionally, we checked if the remaining candidate sites are located within a 200 bp window up- or downstream of any predicted off-target site.

Immunofluorescent staining and confocal laser scanning microscopy

After fixation, tissue was dehydrated in 30% sucrose in PBS for at least 48 hours at 4°C. Tissue was incubated in optimal cutting temperature compound (O.C.T., TissueTek, Sakura Finetek, Torrance, CA) for 24 hours at 4°C and cryoembedded in tissue molds on dry ice. Frozen sections were performed at 7 μm thickness on a cryostat. Antigen retrieval was performed using 0.01 M sodium citrate buffer in PBS (Abcam, Cambridge, MA), followed by blocking for 2 hours in 5% goat serum (Invitrogen, Waltham, MA) in PBS. Sections were then incubated in anti-CD31 antibody (ab28364; Abcam, Waltham, MA) over night at 4°C. Secondary antibodies were applied for 1 hour at room temperature (Goat anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor Plus 647; Thermo Fisher). For staining of cultured DCs, cells in suspension were collected from the culture dishes, spun down, and washed in PBS. Permeabilization was performed using 0.1% Triton X-100 in 1X PBS and cells were incubated at room temperature for 15 min. Cells were again washed and spun down, and blocking was performed using 1% BSA in 1X PBS for 1 h. Cells were incubated in recombinant Anti-NDRG2 antibody (ab174850; Abcam) in 500 µL of 0.1% BSA for 3 h, then washed three times in 1X PBS. Goat anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary Antibody (Alexa Fluor Plus 647, Thermo Fisher) diluted in 500 µL of 0.1% BSA was applied for 45 min. Cells were again washed three times in 1 X PBS, then mounted on slides using cover slips. Imaging was performed on a Zeiss LSM 880 confocal laser scanning microscope at the Cell Science and Imaging Facility at Stanford University. To obtain high resolution images, multiple serial images of 25x magnification were acquired using automatic tile scanning. Individual images were stitched together during acquisition using the ZEN Black software (Zeiss, Oberkochen, Germany).

Quantification of immunofluorescent staining

Immunofluorescent staining was quantified using a code written in MATLAB adapted from previous image analysis studies by one of the authors (K.C.)^47,69. Briefly, confocal images were separated into their RGB channels and converted to binary to determine the area covered by each color channel. DAPI stain, corresponding to the blue channel, was converted to binary using imbinarize and an image-specific, automated threshold determined from the function graythresh in order to optimize the number of DAPI cells counted within the image. For the red and green channels, which corresponded to the actual protein stains, we converted these images to binary with a set a consistent threshold of ~0.3 for all images of the same stain. This consistent threshold insured that our automated quantification of stain area would be unbiased. The area of red and green stains was then normalized by dividing by the number of cells, which we calculated as the number of DAPI nuclei above size threshold of 15 pixels. 3D reconstruction of confocal microscopy images was performed using Imaris 7.2 (Oxford Instruments, Abingdon, UK).

Flow cytometry

Cells were washed in FACS buffer and stained with PE anti-mouse CD135 antibody, Brilliant Violet 421 anti-mouse CD103 antibody, and Alexa Fluor® 647 anti-mouse CD34 antibody (Biolegend, San Diego, CA). Flow cytometry was performed on a BD FACS Aria (Becton Dickinson, San Jose, CA) and data was analyzed using FlowJo (Becton Dickinson, San Jose, CA).

Single cell RNA-seq data processing, normalization, and cell cluster identification

Base calls were converted to reads using the Cell Ranger (10X Genomics, Pleasanton, CA, USA; version 3.1) implementation mkfastq and then aligned against the mm10 (mouse) genome using Cell Ranger’s count function with SC3Pv3 chemistry and 5,000 expected cells per sample^47,48,70. Cell barcodes representative of quality cells were delineated from barcodes of apoptotic cells or background RNA based on a threshold of having at least 300 unique transcripts profiled, less than 100,000 total transcripts, and less than 10% of their transcriptome of mitochondrial origin. Unique molecular identifiers (UMIs) from each cell barcode were retained for all downstream analysis. Raw UMI counts were normalized with a scale factor of 10,000 UMIs per cell and subsequently natural log transformed with a pseudocount of 1 using the R package Seurat (version 3.1.1)⁷¹. Aggregated data were then evaluated using uniform manifold approximation and projection (UMAP) analysis over the first 15 principal components⁷². Cell annotations were ascribed using the SingleR package (version 3.11) against the ImmGen database^73,74 and confirmed by expression analysis of specific cell type markers. Louvain clustering was performed using a resolution of 0.5 and 15 nearest neighbors.

Generation of subpopulation markers, over-representation analysis, and cell-cell communication analysis

Cell-type markers were generated using Seurat’s native FindMarkers function with a log fold change threshold of 0.25 using the ROC to assign predictive power to each gene. Using GeneTrail 3 an ORA was performed for each cell using the 500 most expressed protein coding genes on the gene sets of the Gene Ontology⁴³. P-values were adjusted using the Benjamini-Hochberg procedure and gene sets were required to have between 2 and 1000 genes. To predict intercellular communication between transplanted DCs and local wound cells, scRNA-seq data from Ndrg2-KO DCs and control DCs (Fig 3a) were integrated with data from their specific wound environments (Fig 5a) using Seurat. The CellChat package was used to analyze cellular communication networks using the “Secreted signaling” database⁵³.

Statistical Analysis

Data are shown as mean ± standard error of the mean (SEM) and were analyzed using Prism 8 (GraphPad, La Jolla, CA). Two-group comparisons were performed with Student’s t-test (unpaired and two-tailed). One- or two-way analysis of variance (ANOVA), followed by Benajmini Hochberg post hoc test were performed for comparisons of > 2 groups as appropriate. P < 0.05 was considered statistically significant. The statistical methods used for scRNA-seq analysis are described in the specific sections above.

Animals

All experiments were performed in accordance with Stanford University Institutional Animal Care and Use Committees and the NIH Guide for the Care and Use of Laboratory Animals. The study was approved by the Administrative Panel on Laboratory Animal Care (APLAC) at Stanford University (APLAC protocol number: APB-2965-GG0619). C57BL/6J (wild-type) and C57/BL/6-db/db mice were obtained from the Jackson Laboratory (Bar Harbor, ME). All animal surgeries were performed under inhalation anesthesia with isoflurane (Henry Schein Animal Health) at a concentration of 1-2% in oxygen at 3 L/min. The mice were placed in prone position, and dorsal fur was shaved. Skin was disinfected with betadine solution followed by 70% ethanol three times.

Cell seeding on hydrogels

Cells in suspension were collected from cultures and seeded on pullulan-collagen hydrogels with a diameter of 8 mm at a concentration of 500,000 cells per hydrogel and wound, using a previously described capillary force seeding technique.⁴⁴ We have previously demonstrated that this approach preserves cell viability as well as hydrogel architecture and enables efficient cell delivery.^44,48

Splinted excisional wound model

Splinted full-thickness excisional wounds were created as previously described by Galiano et al. Here, a silicone ring is sutured around the wound margin to stent the skin, mimicking human physiological wound healing by limiting the contraction of the murine panniculus carnosus muscle and allowing the wounds to heal by granulation tissue formation and re-epithelialization.⁷⁵ Two full-thickness dermal wounds of 6 mm diameter were created on the dorsum of each mouse using biopsy punches. A silicone ring was fixed to the dorsal skin using an adhesive glue (Vetbond, 3M, Saint Paul, MN) as well as 8 interrupted 6-0 nylon sutures placed around the outer edge of the ring to prevent wound contraction. Collagen pullulan hydrogels seeded with DCs or blank hydrogels. were placed onto the wound bed and the wounds were covered with sterile dressings (Tegaderm, 3M, Saint Paul, MN). Digital photographs were taken immediately after wounding and placement of the splints and during every dressing change until the time of wound closure. For the hydrogel treatment study, wounds were harvested on day 16 post-wounding, when the wounds in all groups had healed.

Sen, C. K. et al. Human skin wounds: a major and snowballing threat to public health and the economy. Wound Repair Regen 17, 763–771 (2009). https://doi.org:10.1111/j.1524-475X.2009.00543.x
Nussbaum, S. R. et al. An Economic Evaluation of the Impact, Cost, and Medicare Policy Implications of Chronic Nonhealing Wounds. Value Health 21, 27–32 (2018). https://doi.org:10.1016/j.jval.2017.07.007
desJardins-Park, H. E., Gurtner, G. C., Wan, D. C. & Longaker, M. T. From Chronic Wounds to Scarring: The Growing Healthcare Burden of Under- and Over-healing Wounds. Adv Wound Care (New Rochelle) (2021). https://doi.org:10.1089/wound.2021.0039
Veith, A. P., Henderson, K., Spencer, A., Sligar, A. D. & Baker, A. B. Therapeutic strategies for enhancing angiogenesis in wound healing. Adv Drug Deliv Rev 146, 97–125 (2019). https://doi.org:10.1016/j.addr.2018.09.010
Sivaraj, D. et al. Hydrogel Scaffolds to Deliver Cell Therapies for Wound Healing. Front Bioeng Biotechnol 9, 660145 (2021). https://doi.org:10.3389/fbioe.2021.660145
Mushahary, D., Spittler, A., Kasper, C., Weber, V. & Charwat, V. Isolation, cultivation, and characterization of human mesenchymal stem cells. Cytometry A 93, 19–31 (2018). https://doi.org:10.1002/cyto.a.23242
Lipsitz, Y. Y. et al. A roadmap for cost-of-goods planning to guide economic production of cell therapy products. Cytotherapy 19, 1383–1391 (2017). https://doi.org:10.1016/j.jcyt.2017.06.009
Banchereau, J. & Steinman, R. M. Dendritic cells and the control of immunity. Nature 392, 245–252 (1998). https://doi.org:10.1038/32588
Hawiger, D. et al. Dendritic cells induce peripheral T cell unresponsiveness under steady state conditions in vivo. J Exp Med 194, 769–779 (2001). https://doi.org:10.1084/jem.194.6.769
Thurner, B. et al. Generation of large numbers of fully mature and stable dendritic cells from leukapheresis products for clinical application. J Immunol Methods 223, 1–15 (1999). https://doi.org:10.1016/s0022-1759(98)00208-7
Giannoukakis, N., Phillips, B., Finegold, D., Harnaha, J. & Trucco, M. Phase I (safety) study of autologous tolerogenic dendritic cells in type 1 diabetic patients. Diabetes Care 34, 2026–2032 (2011). https://doi.org:10.2337/dc11-0472
Benham, H. et al. Citrullinated peptide dendritic cell immunotherapy in HLA risk genotype-positive rheumatoid arthritis patients. Sci Transl Med 7, 290ra287 (2015). https://doi.org:10.1126/scitranslmed.aaa9301
Bell, G. M. et al. Autologous tolerogenic dendritic cells for rheumatoid and inflammatory arthritis. Ann Rheum Dis 76, 227–234 (2017). https://doi.org:10.1136/annrheumdis-2015-208456
Zubizarreta, I. et al. Immune tolerance in multiple sclerosis and neuromyelitis optica with peptide-loaded tolerogenic dendritic cells in a phase 1b trial. Proc Natl Acad Sci U S A 116, 8463–8470 (2019). https://doi.org:10.1073/pnas.1820039116
Willekens, B. et al. Tolerogenic dendritic cell-based treatment for multiple sclerosis (MS): a harmonised study protocol for two phase I clinical trials comparing intradermal and intranodal cell administration. BMJ Open 9, e030309 (2019). https://doi.org:10.1136/bmjopen-2019-030309
Sawitzki, B. et al. Regulatory cell therapy in kidney transplantation (The ONE Study): a harmonised design and analysis of seven non-randomised, single-arm, phase 1/2A trials. Lancet 395, 1627–1639 (2020). https://doi.org:10.1016/S0140-6736(20)30167-7
Jauregui-Amezaga, A. et al. Intraperitoneal Administration of Autologous Tolerogenic Dendritic Cells for Refractory Crohn's Disease: A Phase I Study. J Crohns Colitis 9, 1071–1078 (2015). https://doi.org:10.1093/ecco-jcc/jjv144
Cheever, M. A. & Higano, C. S. PROVENGE (Sipuleucel-T) in prostate cancer: the first FDA-approved therapeutic cancer vaccine. Clin Cancer Res 17, 3520–3526 (2011). https://doi.org:10.1158/1078-0432.CCR-10-3126
Rathi, N., McFarland, T. R., Nussenzveig, R., Agarwal, N. & Swami, U. Evolving Role of Immunotherapy in Metastatic Castration Refractory Prostate Cancer. Drugs 81, 191–206 (2021). https://doi.org:10.1007/s40265-020-01456-z
Razeghian, E. et al. A deep insight into CRISPR/Cas9 application in CAR-T cell-based tumor immunotherapies. Stem Cell Res Ther 12, 428 (2021). https://doi.org:10.1186/s13287-021-02510-7
Hou, A. J., Chen, L. C. & Chen, Y. Y. Navigating CAR-T cells through the solid-tumour microenvironment. Nat Rev Drug Discov 20, 531–550 (2021). https://doi.org:10.1038/s41573-021-00189-2
Stadtmauer, E. A. et al. CRISPR-engineered T cells in patients with refractory cancer. Science 367 (2020). https://doi.org:10.1126/science.aba7365
Lu, Y. et al. Safety and feasibility of CRISPR-edited T cells in patients with refractory non-small-cell lung cancer. Nat Med 26, 732–740 (2020). https://doi.org:10.1038/s41591-020-0840-5
Srifa, W. et al. Cas9-AAV6-engineered human mesenchymal stromal cells improved cutaneous wound healing in diabetic mice. Nat Commun 11, 2470 (2020). https://doi.org:10.1038/s41467-020-16065-3
Nakano, H., Lyons-Cohen, M. R., Whitehead, G. S., Nakano, K. & Cook, D. N. Distinct functions of CXCR4, CCR2, and CX3CR1 direct dendritic cell precursors from the bone marrow to the lung. J Leukoc Biol 101, 1143–1153 (2017). https://doi.org:10.1189/jlb.1A0616-285R
Harada, S. et al. Flt3 ligand promotes myeloid dendritic cell differentiation of human hematopoietic progenitor cells: possible application for cancer immunotherapy. Int J Oncol 30, 1461–1468 (2007).
Dieu, M. C. et al. Selective recruitment of immature and mature dendritic cells by distinct chemokines expressed in different anatomic sites. J Exp Med 188, 373–386 (1998). https://doi.org:10.1084/jem.188.2.373
Lutz, M. B., Inaba, K., Schuler, G. & Romani, N. Still Alive and Kicking: In-Vitro-Generated GM-CSF Dendritic Cells! Immunity 44, 1–2 (2016). https://doi.org:10.1016/j.immuni.2015.12.013
Choi, S. C. et al. Expression and regulation of NDRG2 (N-myc downstream regulated gene 2) during the differentiation of dendritic cells. FEBS Lett 553, 413–418 (2003). https://doi.org:10.1016/s0014-5793(03)01030-5
Riboldi, E. et al. Cutting edge: proangiogenic properties of alternatively activated dendritic cells. J Immunol 175, 2788–2792 (2005). https://doi.org:10.4049/jimmunol.175.5.2788
Henn, D. et al. MicroRNA-regulated pathways of flow-stimulated angiogenesis and vascular remodeling in vivo. J Transl Med 17, 22 (2019). https://doi.org:10.1186/s12967-019-1767-9
Liu, S. et al. NDRG2 induced by oxidized LDL in macrophages antagonizes growth factor productions via selectively inhibiting ERK activation. Biochim Biophys Acta 1801, 106–113 (2010). https://doi.org:10.1016/j.bbalip.2009.09.022
Hu, W. et al. Emerging role of N-myc downstream-regulated gene 2 (NDRG2) in cancer. Oncotarget 7, 209–223 (2016). https://doi.org:10.18632/oncotarget.6228
Waskow, C. et al. The receptor tyrosine kinase Flt3 is required for dendritic cell development in peripheral lymphoid tissues. Nat Immunol 9, 676–683 (2008). https://doi.org:10.1038/ni.1615
Poltorak, M. P. & Schraml, B. U. Fate mapping of dendritic cells. Front Immunol 6, 199 (2015). https://doi.org:10.3389/fimmu.2015.00199
Cabeza-Cabrerizo, M., Cardoso, A., Minutti, C. M., Pereira da Costa, M. & Reis, E. S. C. Dendritic Cells Revisited. Annu Rev Immunol 39, 131–166 (2021). https://doi.org:10.1146/annurev-immunol-061020-053707
Hiatt, J. et al. Efficient generation of isogenic primary human myeloid cells using CRISPR-Cas9 ribonucleoproteins. Cell Rep 35, 109105 (2021). https://doi.org:10.1016/j.celrep.2021.109105
Riggan, L. et al. CRISPR-Cas9 Ribonucleoprotein-Mediated Genomic Editing in Mature Primary Innate Immune Cells. Cell Rep 31, 107651 (2020). https://doi.org:10.1016/j.celrep.2020.107651
DeWitt, M. A., Corn, J. E. & Carroll, D. Genome editing via delivery of Cas9 ribonucleoprotein. Methods 121–122, 9–15 (2017). https://doi.org:10.1016/j.ymeth.2017.04.003
Bae, S., Park, J. & Kim, J. S. Cas-OFFinder: a fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases. Bioinformatics 30, 1473–1475 (2014). https://doi.org:10.1093/bioinformatics/btu048
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol 38, 1408–1414 (2020). https://doi.org:10.1038/s41587-020-0591-3
Gulati, G. S. et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367, 405–411 (2020). https://doi.org:10.1126/science.aax0249
Gerstner, N. et al. GeneTrail 3: advanced high-throughput enrichment analysis. Nucleic Acids Res 48, W515-W520 (2020). https://doi.org:10.1093/nar/gkaa306
Garg, R. K. et al. Capillary force seeding of hydrogels for adipose-derived stem cell delivery in wounds. Stem Cells Transl Med 3, 1079–1089 (2014). https://doi.org:10.5966/sctm.2014-0007
Rustad, K. C. et al. Enhancement of mesenchymal stem cell angiogenic capacity and stemness by a biomimetic hydrogel scaffold. Biomaterials 33, 80–90 (2012). https://doi.org:10.1016/j.biomaterials.2011.09.041
Wong, V. W. et al. Pullulan hydrogels improve mesenchymal stem cell delivery into high-oxidative-stress wounds. Macromol Biosci 11, 1458–1466 (2011). https://doi.org:10.1002/mabi.201100180
Chen, K. et al. Disrupting biological sensors of force promotes tissue regeneration in large organisms. Nat Commun 12, 5256 (2021). https://doi.org:10.1038/s41467-021-25410-z
Henn, D. et al. Xenogeneic skin transplantation promotes angiogenesis and tissue regeneration through activated Trem2(+) macrophages. Sci Adv 7, eabi4528 (2021). https://doi.org:10.1126/sciadv.abi4528
Michaels, J. t. et al. db/db mice exhibit severe wound-healing impairments compared with other murine diabetic strains in a silicone-splinted excisional wound model. Wound Repair Regen 15, 665–670 (2007). https://doi.org:10.1111/j.1524-475X.2007.00273.x
Liu, Z., Wu, H. & Huang, S. Role of NGF and its receptors in wound healing (Review). Exp Ther Med 21, 599 (2021). https://doi.org:10.3892/etm.2021.10031
Kim, M. H. et al. Galectin-1 from conditioned medium of three-dimensional culture of adipose-derived stem cells accelerates migration and proliferation of human keratinocytes and fibroblasts. Wound Repair Regen 26 Suppl 1, S9-S18 (2018). https://doi.org:10.1111/wrr.12579
Lin, Y. T. et al. Galectin-1 accelerates wound healing by regulating the neuropilin-1/Smad3/NOX4 pathway and ROS production in myofibroblasts. J Invest Dermatol 135, 258–268 (2015). https://doi.org:10.1038/jid.2014.288
Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 12, 1088 (2021). https://doi.org:10.1038/s41467-021-21246-9
Liaw, L. et al. Altered wound healing in mice lacking a functional osteopontin gene (spp1). J Clin Invest 101, 1468–1478 (1998). https://doi.org:10.1172/JCI2131
Sharma, A., Singh, A. K., Warren, J., Thangapazham, R. L. & Maheshwari, R. K. Differential regulation of angiogenic genes in diabetic wound healing. J Invest Dermatol 126, 2323–2331 (2006). https://doi.org:10.1038/sj.jid.5700428
Wang, W. et al. Osteopontin activates mesenchymal stem cells to repair skin wound. PLoS One 12, e0185346 (2017). https://doi.org:10.1371/journal.pone.0185346
Park, J. H. et al. Long-Term Follow-up of CD19 CAR Therapy in Acute Lymphoblastic Leukemia. N Engl J Med 378, 449–459 (2018). https://doi.org:10.1056/NEJMoa1709919
Lundh, S. et al. Clinical practice: chimeric antigen receptor (CAR) T cells: a major breakthrough in the battle against cancer. Clin Exp Med 20, 469–480 (2020). https://doi.org:10.1007/s10238-020-00628-1
Laguette, N. et al. SAMHD1 is the dendritic- and myeloid-cell-specific HIV-1 restriction factor counteracted by Vpx. Nature 474, 654–657 (2011). https://doi.org:10.1038/nature10117
Breckpot, K. et al. Activation of immature monocyte-derived dendritic cells after transduction with high doses of lentiviral vectors. Hum Gene Ther 18, 536–546 (2007). https://doi.org:10.1089/hum.2007.006
Witkowski, W. et al. Vpx-Independent Lentiviral Transduction and shRNA-Mediated Protein Knock-Down in Monocyte-Derived Dendritic Cells. PLoS One 10, e0133651 (2015). https://doi.org:10.1371/journal.pone.0133651
Norton, T. D., Miller, E. A., Bhardwaj, N. & Landau, N. R. Vpx-containing dendritic cell vaccine induces CTLs and reactivates latent HIV-1 in vitro. Gene Ther 22, 227–236 (2015). https://doi.org:10.1038/gt.2014.117
Wong, V. W. et al. Engineered pullulan-collagen composite dermal hydrogels improve early cutaneous wound healing. Tissue Eng Part A 17, 631–644 (2011). https://doi.org:10.1089/ten.TEA.2010.0298
Hopewell, E. L. & Cox, C. Manufacturing Dendritic Cells for Immunotherapy: Monocyte Enrichment. Mol Ther Methods Clin Dev 16, 155–160 (2020). https://doi.org:10.1016/j.omtm.2019.12.017
Lutz, M. B. et al. An advanced culture method for generating large quantities of highly pure dendritic cells from mouse bone marrow. J Immunol Methods 223, 77–92 (1999). https://doi.org:10.1016/s0022-1759(98)00204-x
Donovan, D., Brown, N. J., Bishop, E. T. & Lewis, C. E. Comparison of three in vitro human 'angiogenesis' assays with capillaries formed in vivo. Angiogenesis 4, 113–121 (2001). https://doi.org:10.1023/a:1012218401036
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009). https://doi.org:10.1093/bioinformatics/btp324
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat Methods 15, 591–594 (2018). https://doi.org:10.1038/s41592-018-0051-x
Chen, K. et al. Role of boundary conditions in determining cell alignment in response to stretch. Proceedings of the National Academy of Sciences of the United States of America 115, 986–991 (2018). https://doi.org:10.1073/pnas.1715059115
Chen, K. et al. Mechanical Strain Drives Myeloid Cell Differentiation Toward Pro-Inflammatory Subpopulations. Advances in wound care (2021). https://doi.org:10.1089/wound.2021.0036
Stuart, T. et al. Comprehensive Integration of Single-Cell Data. Cell 177, 1888–1902 e1821 (2019). https://doi.org:10.1016/j.cell.2019.05.031
Diaz-Papkovich, A., Anderson-Trocme, L., Ben-Eghan, C. & Gravel, S. UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts. PLoS Genet 15, e1008432 (2019). https://doi.org:10.1371/journal.pgen.1008432
Mostafavi, S. et al. Variation and genetic control of gene expression in primary immunocytes across inbred mouse strains. J Immunol 193, 4485–4496 (2014). https://doi.org:10.4049/jimmunol.1401280
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 20, 163–172 (2019). https://doi.org:10.1038/s41590-018-0276-y
Galiano, R. D., Michaels, J. t., Dobryansky, M., Levine, J. P. & Gurtner, G. C. Quantitative and reproducible murine model of excisional wound healing. Wound Repair Regen 12, 485–492 (2004). https://doi.org:10.1111/j.1067-1927.2004.12404.x

Table S1

NDRG2 sgRNA	sgRNA Space Sequence	sgRNA targeting Exon/intron	Manufacturer
NDRG2.1.AC	GTAAAAGTGACCGAGCCATA	Exon4	IDT
NDRG2.1.AA	CCAGCCACTGTTTCGGTTCG	Exon5	IDT
NDRG2.1.AE	ACAATAATTGGAGTTGGTGT	Exon7	IDT
NDRG2_1	ATGTTCAGAGCATGGGACCG	Exon2	Synthego
NDRG2_2	GCAGTCTCGGGTGTTTGTCC	Exon2	Synthego
NDRG2_3	CTCCTGAAGTTCTGCCATGG	Intron2	Synthego
Negative control sgRNA
NC1	CGTTAATCGCGTATAATACG		IDT
NC2	CATATTGCGCGTATAGTCGC		IDT
NC3	GGCGCGTATAGTCGCGCGTA		IDT
Primer for PCR and sequencing	Primer sequence
NDRG2.1.AC_For	TCTGCATCCCTCCCATTTATCCTCT
NDRG2.1.AC_Rev	CAGAACCCACCCTCTTGCTCTGATG
NDRG2.1.AA_For	CCATGATGTAGGCCTCAACTGTAAGAC
NDRG2.1.AA_Rev	TCTTCTCTGGAGCAACCTCCGCA
NDRG2.1.AE_For	CAGAGAGCCGAGAATTGATAAACTC
NDRG2.1.AE_Rev	GCAGGAACTCGGACCATTCCTGCAA
NDRG2-3mix-F	CTGGTGTTGCTCTGCACCTTGCATTG
NDRG2-3mix-R	TGGTTACCTGTCCCTGGTCCAGGAG

Table S2

GenomicPosition	REF	ALT	AffectedGene	VEP_Consequence	VariantClass
chr3:113572900	ATG	A	Amy1	intron_variant	deletion
chr3:113572900	ATG	A	Amy1	intron_variant	deletion
chr3:113572900	ATG	A	Amy1	intron_variant	deletion
chr3:113572900	ATG	A	Amy1	intron_variant	deletion
chr4:31726093	T	TG		intergenic_variant	insertion
chr9:89597121	AT	A	Minar1	intron_variant	deletion
chr9:89597121	AT	A	Minar1	intron_variant&NMD_transcript_variant	deletion
chr15:32342480	GA	G	Sema5a	intron_variant	deletion
chr15:32342480	GA	G	Sema5a	intron_variant&non_coding_transcript_variant	deletion
chr15:32342480	GA	G	Sema5a	intron_variant&non_coding_transcript_variant	deletion
chr15:32342480	GA	G	Sema5a	intron_variant&non_coding_transcript_variant	deletion
chr15:32342480	GA	G	Sema5a	intron_variant&non_coding_transcript_variant	deletion
chr15:32342480	GA	G	Sema5a	intron_variant&non_coding_transcript_variant	deletion
chr15:32342480	GA	G	Sema5a	intron_variant&non_coding_transcript_variant	deletion
chrX:8236875	GT	G	Ftsj1	downstream_gene_variant	deletion
chrX:8236875	GT	G	Ftsj1	downstream_gene_variant	deletion
chrX:8236875	GT	G	Ftsj1	downstream_gene_variant	deletion
chrX:48305938	AT	A	Gm7212	upstream_gene_variant	deletion
chrX:48305938	AT	A	Gm22612	upstream_gene_variant	deletion
chrX:52002943	AACAC	A		intergenic_variant	deletion
chrX:63795577	TCA	T		regulatory_region_variant	deletion
chrX:63795577	TCA	T		intergenic_variant	deletion
chrX:73054780	ATC	A	Gm6858	downstream_gene_variant	deletion
chrX:73054780	ATC	A		regulatory_region_variant	deletion
chrX:96613198	TTTG	T		intergenic_variant	deletion
chrX:116833769	GTA	G		intergenic_variant	deletion
chrX:126131620	TA	T	Gm14957	intron_variant&non_coding_transcript_variant	deletion
chrX:136367996	G	GT	Gm14998	downstream_gene_variant	insertion
chrX:136367996	G	GT	Gm14993	upstream_gene_variant	insertion

There is NO Competing Interest.

ExtendedDataFig1.jpg
Supplementary Dataset 1Extended Data Figure 1a, Expression of markers for mature and immature DCs projected onto the UMAP embedding. b, Dot plot indicating Ndrg2 and Vegfa expression in vitamin D3 (VD3) treated and control (untreated) cells. c, Ndrg2 expression in different Seurat clusters of control cells. d, Expression of Ndrg2 (red) and Kit, Csf1r, and Clec9a (blue) projected onto UMAP embedding of cluster 8 subset. Right column shows merged expression of Ndrg2 and co-expressed genes. e, Endothelial cell (EC) tube formation after co-culture of ECs together with dexamethasone stimulated DCs and after EC monoculture in basal media (negative control). f, Zoomed-in image of EC tube formation after co-culture of ECs with VD3-stimulated DCs indicating close interaction of DCs and ECs.
ExtendedDataFig2.jpg
Supplementary Dataset 2Extended Data Figure 2a, Optimization of Cas9 RNP approach using sgRNAs obtained from different manufacturers (Synthego and Integrated DNA Technologies, IDT) at different cell concentrations (1.5 M, 2 M, 3 M). Left: Nucleofection performed with 18.6 pmol Cas9 and 22 pmol sgRNA mix (1 X RNP), right: Nucleofection performed with 27.9 pmol Cas9 and 33 pmol sgRNA mix (1.5 X RNP). b, Alignment between predicted off-target sites and sgRNA sequences (blue). Rows with arrows demonstrate a high alignment. Red arrows indicate potential off-target sites that show a protospacer adjacent motif (PAM) sequence. c, Off-target variant classes. White lines indicate the numbers of different consequences per group.
ExtendedDataFig3.jpg
Supplementary Dataset 3Extended Data Figure 3a, Ndrg2 expression projected onto UMAP embedding of in vitro scRNA-seq dataset, plot split by experimental group. Zoom-in panels showing cluster 9 (Ndrg2+ progenitors). b, Vegfa expression projected onto UMAP embedding of in vitro scRNA-seq dataset, plot split by experimental group. c, Violin plots showing expression of genes (Amy1 and Ftsj1) at potential off-target sites.
ExtendedDataFig4.jpg
Supplementary Dataset 4Extended Data Figure 4a, Masson’s trichrome staining of explanted wound tissue from diabetic mice (day 21). b, Probability of wound closure in diabetic mice after treatment with Ndrg2-KO dendritic cells (DCs), control DCs (nucleofection with 3 non-targeting sgRNAs), and blank hydrogels (reverse Kaplan-Meier estimate, ****P < 0.0001). c, Workflow for possible future clinical application of genetically edited DCs for the treatment of chronic wounds.

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posted 05 Jul, 2022

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Cas9-Mediated Knockout of Ndrg2 Enhances the Regenerative Potential of Dendritic Cells for Wound Healing

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