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Table 11.3 In vivo animal and human bulk RNA sequencing studies related to wound healing
Animal/human
Cell type/tissue
Forearm whole skin Homo sapiens RNA-seq Studied wound healing in DFU and revealed
Human umbilical vein
endothelial cells (HUVEC)
co-culture with murine dermal
broblasts; mouse artery and
vein tissues
Skin Homo sapiens;
Dorsal wound Mus musculus RNA-seq Studied the therapeutic potential of topical murine
Dorsal wound Mus musculus RNA-seq Studied the therapeutic performance of an adhesive patch
Skin Mus musculus RNA-seq Studied the therapeutic potential of topical BRAF
Muscle satellite cells Mus musculus RNA-seq Studied the impairment of regenerative functions in
Skin wound tissue Mus musculus RNA-seq Studied the therapeutic potential of miR146a-loaded
Skin Rattus norvegicus RNA-seq Studied the therapeutic potential of a biological adhesive
Human umbilical vein
endothelial cells (HUVEC)
model
Homo sapiens;
Mus musculus
Streptococcus
agalactiae
Homo sapiens RNA-seq Studied the therapeutic potential of mesenchymal stem
Modality/
platform Outcome References
inammatory biomarkers that correlate with enhanced
wound healing, including angiogenesis regulators
RNA-seq Studied the impact of diabetes on angiogenesis and
demonstrated that the diabetic stroma suppresses
angiogenesis, with extensive transcriptional alterations in
endothelial cells
RNA-seq Studied the group B streptococcus (GBS) infection and
adaptation of the pathogen in the diabetic wound
environment, demonstrating transcriptional changes both
in the host and pathogen
macrophage delivery via alginate dressings in diabetic
wounds, demonstrating enhanced wound healing
that delivers contractions in the vicinity of the wound,
demonstrating enhanced diabetic wound healing by
providing mechanical reinforcement
inhibitor therapy, demonstrating that MAPK activation by
topical BRAF inhibition accelerates wound healing
diabetic mice by extracellular adenosine (eAdo), and
further showed that inhibition of the ENTs-ADK-AMPK
signaling axis is a potential therapeutic target in
eAdo-compromised muscle stem cells
engineered exosomes released from silk broin patch,
targeting IRAK1, and demonstrated the successful
inhibition of the NF-κB signaling pathway invitro, and
enhanced wound healing invivo
developed from snail mucus on diabetic rats, which
exhibited superior tissue adhesion properties, in addition
to promoting healing, epithelial regeneration, and
angiogenesis.
cell-derived exosomes preconditioned with Nocardia
rubra cell wall skeleton (Nr-CWS), which were shown to
promote revascularization, leading to faster diabetic
wound healing
Y. H. Pita-Juarez et al.
Theocharidis
etal. [5];
GSE143735
Singh etal. [6];
GSE181881
Keogh etal. [7];
GSE201342
Theocharidis
etal. [8];
GSE149419
Theocharidis
etal. [9];
GSE154132
Escuin-Ordinas
etal. [10];
GSE148037
Han etal. [11];
GSE175786
Li etal. [35];
GSE217981
Deng etal. [12];
GSE206113
Li etal. [36];
GSE197900
cell lines from mouse dermal broblasts: one from wild-type
mice and one from db/db mice, which closely resemble
T2DM. The diabetic stroma was shown to suppress angiogenesis and result in extensive transcriptional alterations in
endothelial cells, exhibiting over-representation of cytokine
signaling, apoptosis, and collagen degradation-related pathways. The study also discovered a specic subset of stromal
precursor cells (ABCB5+ SPs), which when injected on the
wound site, markedly accelerated the healing process.
Accordingly, aiming to elucidate the impact of pathogen
infection in the diabetic wound, Keogh etal. [7] studied the
Group B Streptococcus (GBS) infection and adaptation of
the pathogen in the diabetic wound environment. The authors
performed bulk RNA-seq in mice with four distinct groups,
split by diabetic status and infection with S. agalactiae. The
dual RNA-seq approach demonstrated transcriptional
changes both in the host and pathogen, revealing that GBS
further promoted inammation in an already hyperinammatory setting. Particularly, they unraveled the complex mechanism of GBS adaptation to the wound environment
by studying GBS expression and identied specic upregulated virulence factors and provided key information in the
development of the diabetic wound’s innate immune
response, which can steer toward inammatory or regenerative wound healing.
Bulk RNA-seq has been also employed to uncover prospective therapeutic avenues within the context of wound
healing or to characterize the therapeutic mechanisms of
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action. Theocharidis etal. [8] explored the therapeutic possibilities of administering murine macrophages, either in primary form or their secretome, topically through alginate
dressings in the treatment of diabetic wounds. The authors
performed bulk RNA-seq in mouse samples utilizing distinct
macrophage populations, in order to study the effects of
treatment across the tissue transcriptome. Alginate dressings
demonstrated desirable properties as a delivery platform for
topical applications, whereas the macrophage treatment
enhanced wound healing, primarily owing to the utilization
of polarized macrophage populations and their secretome.
RNA-seq allowed a deeper interrogation of the effect of macrophage treatment, and highlighted the different wound healing related processes, like keratinization, and anchor
junctioning, whereas vascularization and inammation
related pathways were activated depending on the type of
macrophages that were applied to the wound.
In a subsequent study [9], which aimed to assess the efcacy of a new formulation for a patch to be applied on diabetic wounds, RNA-seq was employed to elucidate the
differences in the transcriptional landscape of the skin
between three groups: no treatment, treatment with a conventional patch, and treatment with the novel formulation.
The new formulation allowed for strikingly rapid wound closure when compared to the conventional one, and RNA-seq
provided potential genes, including Il17a, Il17f, Il19, Il21,
and Il22, painting an inammatory landscape, as well as
pathways of interest, which might be driving this difference
in healing speed, such as processes linked to muscle contraction and metal ion transport.
RNA-seq can also be utilized to study the effects of therapeutic compound treatment, similar to how it was utilized by
Escuin-Ordinas etal. [10]. In this study, wounds were isolated, including both the epidermis and dermis area in db/db
mice treated with vemurafenib or a vehicle control. The
authors highlighted genes that are related to hair follicle
development and proliferation and by integrating the upregulated genes with ChIP-seq data they implicated Tcf7 as a key
transcription factor during wound healing. Similarly, Han
etal. [11] investigated the impairment of regenerative functions in diabetic mice by extracellular adenosine (eAdo), and
further explored therapeutic avenues that combat the effects
of eAdo. The authors of the study conducted bulk RNA-seq
on two distinct groups of muscle satellite cell samples. The
bulk transcriptomic studies uncovered the inhibition of the
ENTs-ADK-AMPK signaling axis through blocking of
either ENTs (equilibrative nucleoside transporters) or ADK
(adenosine kinase) as potential therapeutic targets in eAdocompromised muscle stem cells. Exploring a different therapeutic approach, Li et al. [35] studied the therapeutic
potential of miRNA-loaded engineered exosomes released
from silk broin patch targeting IRAK1 in mice. The
miR146a-loaded exosomes successfully inhibited the NF-κB
signaling pathway invitro by targeting IRAK1 and thereby
reducing inammation. In vivo, the treatment exhibited
wound repair, anti-inammatory, collagen deposition, and
neovascularization properties.
Combining biological engineering with natural properties, Deng etal. [12] studied the therapeutic potential of a
biological adhesive developed from snail mucus on diabetic
rats. The snail mucus-inspired treatment was shown to
exhibit superior tissue adhesion properties, in addition to
promoting healing, epithelial regeneration, and angiogenesis, while reducing inammation. In addition to the upregulation of known key genes for wound healing revascularization
like Vegfb, Tgfb1, Fgf3, and Col7a1, the authors observed
pathways related to wound healing and organ regeneration
being upregulated in the A. fulica d-SMG treated samples.
As shown in the example studies above, bulk RNA-seq
has proven to be an invaluable tool in the eld of diabetic
wound healing research, providing signicant insights into
the underlying mechanisms of this complex process. Bulk
transcriptomic analyses have enabled researchers to characterize the diabetic wound immune microenvironment and
potential therapeutic avenues, including treatment with primary cells, secretomes, compounds, or whole devices.
Through these analyses, key genes and pathways have been
identied, leading to signicant breakthroughs in the eld.
However, this modality is not able to capture the heterogeneity of cell populations within wounds and the cellular interactions involved in the healing process, avenues that have
been recently introduced by single-cell (sc) and spatial transcriptomic methods.
Preclinical Animal andHuman Models
inWound Healing Single-Cell RNA-Seq
Studies
Single-cell methods are powerful modalities to explore cell
type heterogeneity and the complicated interplay in wound
healing [18]. These methods allow for unprecedented granularity in RNA-seq analysis and can elucidate the nergrained aspects of wound healing in skin, such as the
multitude of cell states that are involved and might be transitory or induced after wounding [37]. In this section, we
will summarize some of the recent single-cell studies in
wound healing that aim to analyze the heterogeneity of cell
populations within wounds and have identied unique gene
expression patterns associated with specic cell populations (Table11.4), shedding light on both the intricate biology of wound healing and uncovering potential therapeutic
targets.
A large part of the recent scRNA-seq studies focuses on
understanding cell type heterogeneity during different phases
of wound healing, aiming to clarify the mechanistic differ-
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Table 11.4 In vivo animal and human single-cell RNA sequencing studies related to wound healing
Cell type/tissue
Skin Mus
Skin dermal cells Mus
Neonatal skin Mus
Skin Mus
Antler and back skin Reindeer scRNA-seq and
Skin Mus
Skin epidermal cells Mus
Telogen epidermal cells Mus
Skin epidermal cells Mus
Skin immune cells/bone marrow and PBMCs Mus
Skin epidermal cells Mus
Skin Homo
Foot skin, forearm skin, and PBMCs Homo
Animal/
human model Modality/platform Outcome References
Musculus
Musculus
Musculus
Musculus
Musculus
Musculus
Musculus
Musculus
Musculus
Musculus
sapiens
sapiens
scRNA-seq Studied wound broblast heterogeneity and proposed a
scRNA-seq and
scATAC-seq
scRNA-seq (10×
Genomics)
scRNA-seq (10×
Genomics)
scATAC-seq (10×
Genomics)
scRNA-seq (10×
Genomics)
scRNA-seq
(Fluidigm C1)
scRNA-seq
(Fluidigm C1)
scRNA-seq (10×
Genomics)
scRNA-seq and
ow cytometry
scRNA-seq Revealed that aged mouse skin during wounding
scRNA-seq (10×
Genomics)
scRNA-seq (10×
Genomics)
myeloid-derived broblast subset contributing to
adipocyte regeneration
Characterized the diversity and plasticity of dermal
broblasts during homeostasis and wound healing and
highlighted broblast regenerative capacity
Characterized neonatal papillary broblasts that might
migrate from the wound periphery to the center during
re-epithelialization and can promote healthy skin
regeneration in young skin
Aimed to study semi-regenerative and brotic WIHN
wounds post- wounding. Characterized increased
macrophage inltration and Wnt activity in chronic
inammatory skin conditions
Discovered that uninjured velvet broblasts resemble
human fetal broblasts, which are known to mediate
scarless wound healing, whereas uninjured back
broblasts express inammatory mediators, associated
with brotic wound healing in both human and rodent
skin
Dened sonic hedgehog (Shh) signaling pathway as key
regulator of cell growth and differentiation and highly
involved in WIHN during wound healing
Genetically labeled Lgr5- and Lgr6-expressing cells
from the hair follicle bulge and interfollicular epidermis
and monitored their individual transcriptional proles
during wound healing
Characterized specic interfollicular and follicular
epidermal compartments, assigned cells on an axis from
basal to suprabasal layers and proximal to distal regions
of the hair follicles and highlighted how cellular
heterogeneity can be orchestrated invivo to assure
tissue homeostasis
Proposed a revised “hierarchical-lineage” model of
homeostasis by capturing the epidermal basal cellular
dynamics in wound healing
Dened that the Angptl4 deciency reduces monocytederived macrophages and substantially prolongs the
inammatory phase of wound healing
exhibits a more inammatory phenotype, higher
proportions of neutrophils, Arg1 high macrophages
which potentially communicate with broblasts using
the IL1 axis possibly contributing to the delayed healing
observed in aged skin
Characterized secretory-papillary, secretory-reticular,
pro-inammatory and mesenchymal broblasts along
with their activity in normal scar, keloid and
scleroderma human skin samples
Characterized the transcriptional proles in patients that
were either healthy, suffered from diabetes, or had
diabetes and diabetic foot ulcers (healers and
non-healers). Identied a rare broblast population in
healers, higher abundance in M1 macrophages and
lower M2 anti- inammatory macrophages
Y. H. Pita-Juarez et al.
Guerrero- Juarez
etal. [38]
Abbasi etal. [39]
Phan etal. [40]
Gay etal. [41]
Sinha etal. [42]
Lim etal. [43]
Joost etal. [44]
Joost etal. [45]
Haensel etal.
[46]
Wee etal. [47]
Vu etal. [48]
Deng etal. [49]
Theocharidis
etal. [50]
ences between a wound that becomes brotic and highly
inammatory or one that heads for complete regeneration
[18]. Several of these studies focus on broblast diversity in
dermal and epidermal tissues, and key cell subpopulations
crucial for the tissue regeneration during wound healing. For
instance, Guerrero-Juarez et al. [38] studied wound bro-
blast diversity and heterogeneity by pooling wound tissue
from the skin of 12 mice and performed scRNA-seq on their
post-wounding tissues. Twelve broblast subpopulations
were identied, most composing a contractile phenotype,
while others appeared as distinct broblast lineages through
pseudotime and RNA velocity analysis. Of special interest
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was a subset of broblasts that expressed hematopoietic
markers, suggesting myeloid origin, which was later validated using single-cell western blot, Cre recombinase-based
lineage tracing, and full-length scRNA-seq of genetically
modied myobroblasts. The study pointed out the large heterogeneity among broblasts during wound healing and the
existence of myeloid-derived broblast subsets that contribute to adipocyte regeneration.
Likewise, Abbasi et al. [39] aimed to characterize the
diversity and plasticity of dermal broblasts during homeostasis and wound healing. The authors used a mouse model
of skin injury that can result in scar formation or regeneration and performed scRNA-seq and scATAC-seq on 12days
post-wounding tissues. scATAC-seq is a technique enabling
the identication of accessible chromatin regions in the promoters of genes. In tandem with scRNA-seq, it is a powerful
assay for the discovery of key transcription factors regulating
the cellular phenotypes and transcriptional programs [51].
They observed that hair follicle mesenchymal progenitors,
stem cells that reside in the hair follicle niche, contribute
minimally to wound repair. In contrast, extrafollicular progenitor cells were highly expressing Hic1, a quiescenceassociated marker gene and produced most of the regenerative
broblasts, as well as exhibited distinct functional heterogeneity depending on their location within the wound.
Specically, they observed that progenitors located in the
center of the wound tend to acquire mesenchymal regenerative competence, supporting skin and hair follicle regeneration, while peripheral ones promote scar formation. Using
scATAC-seq, the authors also identied changes in chromatin accessibility within this specic regeneration-related
locus and further investigated broblast regenerative capacity. By integrating scRNA-seq with scATAC-seq, they
assessed how distinct transcriptional, regulatory, and epigenetic proles controlling the acquisition of those mesenchymal regenerative properties, including the Wnt, BMP, and
TGF beta pathways. They subsequently focused on Runx1, a
transcription factor that regulates cell differentiation and
migration and retinoic acid signaling, dening it as a key factor that enhances mesenchymal regenerative competence,
while they orthogonally validated their ndings using inhibition with pharmacological agents, as well as a knockout of
Hic1 gene.
Focusing on hair follicles, Phan etal. [40] studied woundinduced hair follicle (WIHN) neogenesis. In WIHN, hair follicles are formed mainly during large wounds and not during
small wounds. The authors performed scRNA-seq in murine
skin to compare the molecular proles of small scarring
wounds versus larger regenerative wounds and dene cell
types that promote healthy skin regeneration. A specic subset of neonatal papillary broblasts, termed upper wound
broblasts, was identied, upregulating Lef1, and was highly
enriched in regenerative wounds. These cells also expressed
the retinoic binding protein Crabp1 and resembled papillary
broblasts, which are known to support hair follicle formation and maintenance. RNA velocity analysis was applied
along with an integrated pathway analysis to not only place
these broblast subsets across an inferred differentiation trajectory, but also to characterize the active pathways in each
one. The authors specically identied that upper wound
broblasts were closely associated with dermal papilla cells,
terminally differentiated broblasts that reside at the base of
hair follicles, and suggested a model where papillary broblasts might migrate from the wound periphery to the center
during re-epithelialization.
Similarly, Gay etal. [41] aimed to study semi- regenerative
and brotic WIHN wounds at different post-wounding mice
skin tissues and characterize the WIHN-induced regenerative repair response. The authors performed scRNA-seq in
mice WIHN+ and WIHN− dermal cells. They identied that
macrophages can inuence wound healing fate by modulating Wnt signaling. Notably, in brotic wounds, macrophages
expressed higher levels of genes involved in phagocytosis
and degradation than in semi-regenerative wounds, while
myobroblasts, cells that can produce scar tissue, were more
abundant. The authors also reported and validated a key factor for wound regeneration, Sfrp4, a Wnt inhibitor protein,
highly expressed in the dermis of semi-regenerative wounds
compared to brotic wounds.
In a subsequent re-analysis of this study, Chen etal. [37]
found that myobroblasts and macrophages were the core
cell subpopulations that were controlling key pathways such
as proliferation, cell migration, and TGF beta through ligandreceptor interactions and reconstructed the dynamics of
myobroblasts and macrophages using trajectory pseudotime inference. The authors provided a model for these cell
population potential underlying evolution and budding functional heterogeneity. In a second integrative analysis using
publicly available datasets, Phan et al. [43] compared the
function of upper and papillary wound broblasts. The
authors focused on RNA velocity analysis and showed that
upper wound broblasts resided on a different developmental pseudotime trajectory than lower wound broblasts with
a higher differentiation from terminally differentiated into
dermal papilla, reconciling previous inconsistencies between
lineage tracing and scRNA-seq data.
In an elegant study that sought to investigate the interaction between scarring and hair follicle regeneration, Lim
etal. [43] aimed to determine whether scarring could promote wound healing with hair follicle regeneration and without brotic repair, highlighting yet another important cell
type involved in the wound healing process. The authors
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Y. H. Pita-Juarez et al.
applied scRNA in mouse skin to specically examine sonic
hedgehog (Shh) signaling, a pathway that regulates cell
growth and differentiation, and its involvement in WIHN
during mouse skin wound healing. They utilized different
genetic tools to manipulate Shh signaling and nd that Shh
has distinct roles in different broblast subpopulation functions. In reticular broblasts, which normally reside in the
deeper layers of the dermis, Shh promoted the initial phase
of wound repair, modulating angiogenesis and proliferation.
In papillary broblasts, usually in the supercial layer of the
dermis, Shh is essential to induce hair follicle neogenesis
(HFN) in the healing wound, as suggested by the high
expression of Gli1, a TF mediator of hedgehog signaling.
Stem cells (SCs) are also being actively studied in woundhealing research [52, 53] and regenerative medicine [54]. For
instance, Joost etal. [44] aimed to investigate SCs in skin
epithelium and their response and adaptation to wound healing in mice. The authors focused on SCs that expressed Lgr5
or Lg6, genes that regulate the Wnt signaling pathway. Lgr5-
expressing SCs are located in the hair follicle bulge, and
Lgr6-expressing SCs are found in the interfollicular epidermis and can migrate to different locations, as well as differentiate into several cell types. Using scRNA-seq and
genetically labeled cells, they collected wounded and
unwounded skin from mice at different time points and found
that Lgr5 and Lgr6 progeny induced a gene expression signature related to inammation, proliferation, migration, and
extracellular matrix (ECM) remodeling, expressing several
different receptors that allowed them to interact with the
wound environment such as EGF, FGF, PDGF, and IL-6.
They also observed a transition of the Lgr5 progeny to
assume an interfollicular epidermis identity, revealing that
the two populations were differentially primed for response
to wounding.
Joost etal. [45] isolated and assayed cells from the telogen epidermis of adult mice to study how cellular heterogeneity is tuned at the transcriptional level. The authors
identied 25 distinct cell populations and assigned each of
them to a specic interfollicular and follicular epidermal
compartment based on their gene expression proles. Using
computational reconstruction methods, they assigned cells
on an axis from basal to suprabasal layers and proximal to
distal regions of the hair follicles and showed that transcriptional heterogeneity of the epidermis can be explained in
large part by these two axes of differentiation and spatial
localization. They also highlighted a specic gene module
that denes basal-epidermal identity in stem cell populations
and reects their niche-specic functions based on their
localization.
Subsequently, in a relevant study, Haensel et al. [46]
aimed to characterize the epidermal basal cellular dynamics
during differentiation in wound healing. The authors performed scRNA-seq in unwounded mouse back skin and
4days post-wounding, corresponding to a stage of active reepithelization. They focused on four distinct (non-) proliferative basal cell states whose expression was signicantly
changed during wound healing, highly heterogeneous in
their molecular proles, including differences in genes
related to inammation, migration, quiescence, cell cycle
arrest, and cell differentiation. Specically, they characterized (1) a Col17a1Hi cell population presenting a “quiescence
and stemness” signature, highly expressing Col17a1 and
Trp63, a marker usually highly expressed in epidermal stem
cells, as well as a marker gene enriched in quiescent bulge
hair follicle stem cells (Bu-HFSCs), (2) an early-response
subpopulation upregulating immediate early genes and genes
related to Bu-HFSCs or with known function in regulating
proliferation, (3) a growth-arrested cell type, highly inammatory, upregulating genes related to TNF-a signaling,
hypoxia, and epithelial-to-mesenchymal transition, as well
as genes associated with cell cycle arrest and (4) a high proliferative cell population, substantially different in metabolic
preference. During wound re-epithelialization, these cell
states become spatially partitioned, as identied by RNA in
situ using RNAscope and uorescence lifetime imaging
microscopy (FLIM). The authors nally proposed a “hierarchical lineage” of homeostasis, driven by these cell states,
delineated through trajectory and velocity analysis, suggesting that post-wounding, skin epidermal cells are more active,
with increased plasticity and relaxed differentiation compared to the unwounded cells.
Inammatory response at the wound site is a key factor in
non-healing wounds [55, 56] . Wee et al. [47] studied the
immune component in the wound site that delays the transition from the inammatory to the wound healing phase. The
authors focused on skin immune cells isolated from bone
marrow and Peripheral Blood Mononuclear Cells (PBMCs)
and performed scRNA-seq to resolve their role during wound
healing. They revealed a novel role for Angiopoietin-like 4
protein (Angptl4), a protein that regulates monocyte differentiation through an interferon-mediated axis, and more specically, interferon-activated gene 202B (i202b). Utilizing
a combination of ow cytometry and scRNA-seq, they
deeply characterized the immune cell landscape of excisional wounds from wild-type and Angptl4 knockout mice.
They showed that Angptl4 deciency does not affect the
recruitment of immune cells but instead reduces monocytederived macrophages and substantially prolongs the inam-
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matory phase of wound healing. With a kinase inhibitor
screen, they identied several kinases that mediate Angptl4
signaling and i202b expression, such as JAK and CDK, and
orthogonally validate their ndings with an invitro reconstituted wound microenvironment, silencing i20b expression
in knockout mice, leading to impaired monocyte
differentiation.
Finally, Vu et al. [48] aimed to uncover systems-level
alterations in its cellular composition and cell-cell communication during wound healing. The authors specically compared skin from adult and aged mice by performing
scRNA-seq to capture the epithelial, broblast, and immune
cell types and their respective heterogeneity in young versus
aged skin during homeostasis. Their study revealed that aged
mouse skin during wounding exhibits a more inammatory
phenotype, featuring higher proportions of neutrophils, as
well as Arg1 high macrophages, which potentially communicate with broblasts using the IL1 axis. Concurrently, broblasts were reduced in aged skin, and found widespread
alterations in ECM-related gene expression, possibly contributing to the delayed healing observed in aged skin. One
promising candidate gene that assists in wound healing
through broblast activation in humans and identied in this
study was Ccl19. This study also highlights another powerful
analysis unlocked by single-cell wound healing, the interrogation of cell-cell interactions at the single-cell level.
Moving away from murine investigations, Sinha et al.
[41] proposed a wound healing investigation at the single
cell level in another animal model, the reindeer, to examine
how antler skin (velvet) and back skin differ in wound healing and to identify how broblasts can inuence wound healing outcomes. The authors, by integrating scRNA-seq and
scATAC-seq, compared the gene expression, chromatin
accessibility, and protein abundance proles of different cell
types in uninjured and injured velvet, as well as back skin.
They discovered that uninjured velvet broblasts resembled
human fetal broblasts, which are known to mediate scarless
wound healing, whereas uninjured back broblasts expressed
inammatory mediators associated with brotic wound healing in both human and rodent skin. Injury elicited sitespecic immune responses, with back skin broblasts
recruiting more myeloid cells, in comparison to velvet skin
broblasts, which adopted an immunosuppressive phenotype, restricting leukocyte recruitment while hastening
immune resolution using distinct expression and epigenetic
programs, regulating cytokines, chemokines, and their receptors. Using ectopic transplantation of broblasts to scarforming back skin, they showed that while there is initial
regeneration post wounding, velvet broblasts progressively
transition to a brotic phenotype, similar to a fetal to the
adult transition of broblasts reported in humans.
Apart from animal models, several wound healing studies
on human tissues have been performed recently. One of the
rst was by Deng etal. [49], who aimed to characterize broblast populations in the normal scar, keloid, and scleroderma
human skin samples. The researchers identied four large
broblast subsets, dened by their gene expression proles,
separated into secretory-papillary, secretory-reticular, proinammatory, and mesenchymal broblasts. Proinammatory cells characterized by high expression of genes
related to migration, invasion, and ECM, signicantly
increased in keloid and scleroderma compared to normal
scars. In addition, the pro-inammatory broblasts, characterized by high cytokine production and robust immune
response, signicantly increased in scleroderma compared to
normal scar, as well as keloid. To validate the role of the
mesenchymal broblasts, the authors used uorescenceactivated cell sorting (FACS) to isolate them based on expressions and co-cultured them with normal scar broblasts,
discovering that the mesenchymal broblasts induced collagen overexpression in normal scar broblasts and performed
lentivirus-mediated knockdowns of key genes in important
mechanisms aiming to reduce the collagen overexpression.
Likewise, more recently, Theocharidis et al. [50] aimed to
characterize the transcriptional proles in patients that were
either healthy, suffered from diabetes, or had diabetes and
diabetic foot ulcer patients, splitting the latter category into
healers and non-healers. The authors performed scRNA-seq
to assay foot skin, forearm skin, or PBMCs. By creating this
diabetic wound healing cell atlas enabled them to compare
the cells from patients whose ulcers had healed and those
with chronic wounds, and identied a unique broblast subpopulation, termed healing broblasts, which upregulated
genes involved in matrix degradation, hypoxia response, and
inammation. They also identied a higher abundance of M1
macrophages, which were more inammatory and promoted
wound healing, while non-healers had higher M2 macrophages, which were anti-inammatory in diabetic healer
patients.
In summary, single-cell RNA sequencing studies have
already been embraced in skin and wound healing research
in particular, allowing researchers to closely examine several
aspects of tissue, such as cell type diversity and heterogeneity, identifying rare cell types post-wounding with a critical
role in tissue regeneration, as well as characterizing complex
cell-cell communication interactions post-wounding.
However, these studies had largely overlooked the spatial
organization of cells within the wound microenvironment.
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Y. H. Pita-Juarez et al.
For example, a key limitation of cell-cell communication
analysis in single-cell RNA-seq is that since the cells’ spatial
information is lost, many pathways might be inferred by
algorithms that are in cells who are not physically proximal.
The integration of the recently introduced spatial tissue proling, and especially spatial transcriptomic assays, offers
valuable insights into this missing dimension, providing a
crucial understanding of the spatial organization of cells
within the wound microenvironment and signicantly
enhancing our comprehension of wound healing processes.
Spatial Transcriptomics inWound Healing
Spatial transcriptomics is a new avenue for wound healing
research and offers a promising approach by providing a
detailed understanding of tissue morphology and spatial organization. Even though it’s a new research modality, ST has
started being increasingly utilized to support cutting- edge
wound healing research. The very rst ST studies in both
human and animal models have focused on understanding the
cellular interactions involved in the wound-healing process,
identifying potential therapeutic targets, and developing strategies for promoting tissue regeneration. By utilizing spatial
transcriptomics, researchers aimed to analyze the gene
expression patterns of various cell types within the wound
microenvironment, providing insights into the complex cellular interactions involved in the healing process [1, 23].
Theocharidis etal. [50] showed enrichment of a unique
population of broblasts overexpressing MMP1, MMP3,
MMP11, HIF1A, CHI3L1, and TNFAIP6 and increased M1
macrophage polarization in the diabetic foot ulcer patients
with healing wounds. The authors used the Nanostring
GeoMx DSP ST platform to spatially characterize the transcriptome of wound healing tissue samples and discovered
preferential localization of these healing-associated broblasts toward the wound as opposed to the wound edge or
unwounded skin. Co-localization with CD68+/CD80+ M1
macrophages was also observed in healers, whereas
broblasts enriched in non-healers co-localized with M2
macrophages which were CD163+. The study sheds light
on the wound healing microenvironment, identifying cell
types that may be important in promoting DFU healing and
informing potential therapeutic approaches for DFU
treatment.
ST has also been utilized to investigate wound healing in
mice. Foster etal. [57] employed multi-omics to comprehen-
sively study the cells involved in wound healing across time
and space, using a stented wound model that aims to recapitulate human tissue repair kinetics in mice. To characterize
broblast subpopulations during the physiologic response to
injury and their spatial location, the authors combined
scRNA-seq, scATAC-seq, and ST 10× Genomics Visium
data. This platform enables the capture of whole transcriptome information at 55 um resolution. Through this multimodal scRNAseq and ST investigation, they discovered four
broblast subpopulations with distinct functions during the
wound healing progress: migratory broblasts that move to
the wound site, proliferative broblasts that drive expansion,
contractile broblasts that contribute to wound closure, and
ECM-producing broblasts that deposit scar tissue. They
identied several transcription factors and signaling pathways controlling broblast fate during wound healing,
including Tcf4, Cebpd, Tgfb1, and the Wnt pathway. They
proposed a dynamic model where broblasts adapt to tissue
disruption and provided new insights into tissue repair and
brosis.
Spatial transcriptomic techniques are poised to revolutionize wound healing research by revealing the complex
processes of tissue repair and regeneration. These studies
enhance our understanding of intricate cellular interactions
and hold tremendous potential for advancing our knowledge
and treatment options in wound healing.
scRNA-Seq andSpatial Omics Analysis:
Description oftheResearch Pipelines
Adopted inWound Healing Studies
Following tissue pre-processing and the application of the
scRNA-seq or ST assay, the major bottleneck is data analysis
and derivation of biological information. In this section, we
will present a step-by-step overview for the analysis of
scRNA-seq and spatial transcriptomic datasets (Figs. 11.3
and 11.4) and we will focus on the specic workows
adopted in wound healing studies. We hope that this section
will familiarize the readers with the processes involved and
the specic aims of each key task, without going into technical details. This information could be used to evaluate how
studies have been performed and whether all technical considerations have been taken into account or as a guide for
future studies. We also highlight key challenges and aspects
that are central for the successful utilization of such
experiments.
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Fig. 11.3 Overview of best-practices for single-cell transcriptomic
analysis (scRNA-seq). (a) Upon raw data processing, a count matrix of
cells by genes is obtained per sample, which is then subjected to quality
control. Ambient RNA removal, doublet detection, and empty droplet
removal are subsequently performed. Low-quality cells are removed
during the quality control step, by ltering outliers with respect to the
read depth of the library, the number of counts and number of genes per
barcode, and the mitochondrial content. Normalization and feature
selection are performed accordingly, while dimensionality reduction
and batch correction methods are applied to facilitate visualization,
reduce computational costs, and decrease the effect of noise. (b)
Following the aforementioned adjustments, the corrected expression
space undergoes clustering to enable the biological grouping of cells
with similar expression proles. Cluster annotation is carried out, either
by following automatic annotation approaches or by manually annotating the cluster based on prior knowledge of cell-type markers. Cell fate
is also studied by performing trajectory inference analyses, which in
turn can inform about cellular diversity. (c) Further processing can be
carried out to answer specic biological questions, where appropriate.
For example, differential expression analysis can reveal upregulated or
downregulated genes in the data set, gene set enrichment analysis can
uncover pathway-wide effects, whereas compositional analysis can
bring to light differential cell-type compositions between groups. In
more advanced settings, perturbation modeling allows for the study of
induced perturbations, cell-cell communication inference analysis can
unveil interactions between annotated cell-types, and gene regulatory
networks can also be established
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Fig. 11.4 Spatial transcriptomics analysis workow. (a) Depending on
the assay, the raw data processing step varies. For in situ capture
sequencing, raw sequencing data are processed into count matrices consisting of genes and capture spots. Fluorescence in situ hybridization,
on the other hand, entails converting uorescent imaging signals into
similar count matrices. The count matrices are preprocessed to obtain
quality control metrics such as distributions of UMIs per spot (counts
per spot) and genes per spot, as well as data normalization.
Dimensionality reduction methods include summarization and visualization methods (e.g., PCA, UMAP), both of which seek to reduce gene
expression data into fewer dimensions for more informative analysis.
(b) Similar spot transcriptomes are grouped together through clustering, and these transcriptomes can then be superimposed over the original tissue images for broad interpretation. (c) Mapping and
deconvolution combine scRNA-seq data with spatial transcriptomics
data to locate cell subpopulations. Deconvolution is typically used to
infer cell-type proportions per capture spot from an annotated scRNAseq data set. (d) The cell type maps generated via mapping and deconvolution can be used to perform ligand-receptor analyses. The proximity
of cell types can aid in determining cell-cell communication events
d
Analysis ofscRNA-Seq Experiments
The section will cover several steps of the analysis, including
data pre-processing, feature selection, data integration, and
dimensionality reduction. We will present clustering approaches
to dene cell subpopulations and tools for cell- type characterization. Part of the functional analysis will also be presented,
including cell type proportional differences among the distinct
cell populations, pseudotime inference, identication of important signaling pathways of the different cell types, and characterization of important cell-cell communication interactions
often described in wound healing studies (Fig.11.3).
quality cells and genes and to correct for technical biases
(Fig.11.3a). Low-quality cells may have high levels of mitochondrial RNA, low gene expression levels, or low complexity, which is measured as the number of genes they are
expressing. Another important consideration is the possible
need for decontamination of the data, in case there is substantial ambient RNA present (RNA not belonging to a cell).
This might be due to debris leading to wrong cell counting,
cells that are lysed during library preparation or extensive
degradation. These issues can be addressed computationally
using decontamination methods, which aim to denoise
expression coming from empty droplets by modeling it out
using Bayesian methods [58], or deep generative modeling
[59]. Another technical issue that can arise in single-cell
From Raw Data toHigh-Quality Cellular
Proles: Pre-processing andSample
Integration
RNA-sequencing is multiplets, where two or more cells can
get trapped in the same gel beads in emulsion (GEM). These
can be delineated using their expression [60, 61] and are
often referred to as cell doublets or multiplets.
Pre-processing involves data, quality control, normalization,
and ltering of the raw scRNA-seq counts to remove low-
A critical step in scRNA-seq analysis is sample integration and dimensionality reduction (Fig.11.3a). The rst step
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includes correcting for technical batch effects that may arise
from differences in sample preparation or sequencing protocols [62]. Batch effect correction can be applied either to the
gene expression level, (methods such as, e.g., ComBat-seq
[63], RUV [64], and Seurat's integration pipeline [65], or in
embedding space [62]. In dimensionality reduction, the aim
is the representation of the high-dimensional gene expression data (thousands of gene expression measurements)
through a lower dimensional representation (e.g., two dimensions to enable easy review and visual inspection) using
methods such as principal component analysis (PCA),
t- distributed stochastic neighbor embedding (t-SNE), and
Uniform Manifold Approximation and Projection for
Dimension Reduction (UMAP). Dimensionality reduction
facilitates the visualization and clustering of cells based on
their gene expression proles.
From Clusters toCell Identities: Cell-Type
Annotation at Single-Cell Resolution
Cell-type annotation at single-cell resolution is one of the
most important critical steps in scRNA-seq analysis. It refers
to the identication and annotation of the different cell types
present in a heterogeneous population of cells based on their
gene expression proles at the single-cell level and can provide insights into the molecular mechanisms underlying cellular processes and disease states (Fig. 11.3b). Cell-type
annotation can be performed using various methods,
including marker genes, machine-learning, and referencebased annotation approaches.
Reference datasets, i.e. gene expression cell proles
which can be utilized to identify and annotate unknown cells,
can be obtained from publicly available databases, such as
the Human Cell Atlas [66], or can be generated using scRNAseq data from known cell types, publicly available or inhouse generated. Prominent examples of tools aiming to
automatically annotate cells based solely on their transcriptomes and reference cell information are scANVI [67],
SingleR [68], Celltypist [69], onClass [70], Symphony [71]
and singleCellNet [72].
Functional Downstream Analysis: Revealing
Mechanisms
Pseudotime inference and RNA velocity have been broadly
studied for cellular differentiation and cell state transitions
during wound healing [38]. Trajectory analysis can be used
to identify intermediate states or transitional cell types along
developmental pathways and to infer the regulatory mechanisms that drive cellular differentiation (Fig.11.3b). Several
methods have been developed in the past few years, includ-
ing Monocle [73–75], Slingshot [76], and CellRank [77].
These approaches can identify branch points and intermediate cell types along developmental trajectories and can be
used to visualize the expression of genes over time.
Complementing this information, RNA velocity can infer the
direction and speed of gene expression changes in individual
cells over time. RNA velocity can be used to predict future
cell states and to identify intermediate states or transitional
cell types along developmental trajectories. Tools like
Velocyto [78] and scVelo [79] are two of the most broadly
used that utilize spliced and unspliced read counts from
single- cell RNA sequencing data to estimate the velocity of
gene expression changes in individual cells.
Other steps of analysis, including ligand-receptor cell-cell
communication analysis, co-expression analysis, and gene
set enrichment analysis (GSEA), are also critical for the
identication and characterization of signaling pathways and
interactions between cells in complex biological systems
(Fig.11.3c). Ligand-receptor analysis can be used to identify
putative ligand-receptor pairs between different cell types,
providing insights into potential signaling pathways and cellular interactions. Several tools are available for ligandreceptor analysis, including CellPhoneDB [80], nicheNET
[81], and cellChat [82]. On the other end, co-expression
analysis can be used to identify groups of genes that are coexpressed across different cell types, providing insights into
potential signaling pathways and cellular interactions [83].
Relevant tools dedicated to co-expression analysis are
WGCNA [84] and Monocle [73, 75]. Finally, gene set enrich-
ment and pathway analyses can be used to identify signaling
pathways and cellular functions that are enriched in specic
cell types or subpopulations, providing insights into potential signaling pathways and cellular interactions. Gene set
enrichment analysis can be performed using tools such as
GSEA [85] and clusterProler [86].
Analysis ofSpatial Transcriptomics
Spatial transcriptomics analysis follows similar principles
for single-cell RNA-seq analysis. In general, the analysis can
be divided in two major phases: preprocessing and downstream analysis (Fig.11.4).
Preprocessing
The objective of preprocessing is to ensure that high-quality
data are available for downstream analysis, with the goal to
discover the biological implications of the data. Preprocessing
involves a number of steps, such as dimensionality reduction, normalization, and quality control (Fig.11.4a). Quality
control is paramount to obtaining actionable results from
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