Disease-related biomarkers as experimental endpoints in 3D skin culture models

Abstract

The success of in vitro 3D models in either recapitulating the normal tissue physiology or altered physiology or disease condition depends upon the identification and/or quantification of relevant biomarkers that confirm the functionality of these models. Various skin disorders, such as psoriasis, photoaging, vitiligo, etc., and cancers like squamous cell carcinoma and melanoma, etc. have been replicated via organotypic models. The disease biomarkers expressed by such cell cultures are quantified and compared with the biomarkers expressed in cultures depicting the normal tissue physiology, to identify the most prominent variations in their expression. This may also indicate the stage or reversal of these conditions upon treatment with relevant therapeutics. This review article presents an overview of the important biomarkers that have been identified in in-vitro 3D models of skin diseases as endpoints for validating the functionality of these models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10616-023-00574-2.

Introduction

Skin is an intricate, the most accessible and the largest protective organ system of the human body. Its functions include sensory detection, thermoregulation and fluid homeostasis. It passively provides first line of defence i.e. innate immunity preventing entry of immunogenic substances by forming a three layered structure (namely epidermis, dermis and hypodermis) (Abdo et al. 2020). Due to its large surface area, barrier defense property and its capacity for extensive drug delivery, it is an important organ for cosmetics and pharmaceutical industries (Yu et al. 2021). Consequently, in-vivo replication of skin diseases (SDs) and evaluation of their pathophysiology becomes crucial for determining drug dose, safety and designing their formulations (Hassan et al. 2014; Varma et al. 2018). Alternative approaches for use of animals in disease modelling and pre-clinical evaluations have been rigorously revised after the amendment of laws by “International Cooperation on Alternative Test Methods” (ICATM) and “Organization for Economic Cooperation and Development” (OECD) (Badyal and Desai 2014). Evidently, there is a need to develop more methods for recaptulating SDs under in-vitro conditions, such as in commercially available, reconstructed human epidermis models (RHE) like SkinEthic™, EpiSkin™, Phenion® epiCS, StratiCELL, StrataTest®, LabCyte EPI-MODEL and reconstructed human full-thickness skin models (FT) such as Vitrolife-Skin™, Phenion® FTskin, EpiDerm-FT™, CELLnTEC FTskin, Biomimiq FTskin, T-Skin™, or by developing the skin-on-chip (SOC) technology (Netzlaff et al. 2005; Carlson et al. 2008; Wufuer et al. 2016a;  Alépée et al. 2019; Nguyen and Pentoney 2017; Zhang et al. 2018; Kim et al. 2019; Bataillon et al. 2019).

In early 2000s, in-vitro human skin models (IHSMs) were majorily constructed using either primary or cell lines of human skin fibroblasts (FBs) and keratinocytes (KCs). But because of the rapid evolution in the tissue engineering sector and introduction of bioprinting technology, IHSMs can now be constructed by integrating melanocytes, adipocytes, and endothelial cells (Randall et al. 2018). Thus, SDs have been recapitulated via numerous IHSMs, ranging from RHE, collagen-hydrogel based self-assembled organoids and SOC models. These exhibit varying utility for modeling complex SDs (Sarkiri et al. 2019). While constructing any in-vitro human skin diseased models (IHSDMs), it is important to understand the pathophysiology of the disease to be able to mimic the triggers in the organotypic model. Various diseases can be induced in 3D cellular models by including effected cells isolated from patients or by insertion of disease-causing genes/factors/molecules in the cultured cells or by exposure to disease-inducing signalling molecules (Nguyen and Pentoney 2017). These markers related to these SDs can be analysed to investigate alterations in and around skin cells during disease progression, which affect tissue regeneration, physiochemical function and barrier properties (Metcalfe and Ferguson 2007; Paliwal et al. 2013; Dreesen and Wang 2018).

IHSMs should express specific biomarkers, as stated in Table 1, for mimicking the characteristics of young native human skin. These include fibrillin 1, pro-collagen I etc. (Vörsmann et al. 2013; Hill et al. 2015; Randall et al. 2018; Moniz et al. 2020). Some markers, such as melan-A melanocyte etc. are useful for tracking the diseased cells as well as for understanding changes in proliferation and/or differentiation that may occur in skin cells because of the disease. These have been listed in Table 2. Recent advances in the use of machine learning and more clinically significant statistical tools for assessment of disease severity has facilitated identification of measurable biomarkers to standardize those associated with diseased and healthy skin. It has also allowed researchers to correlate and quantify biomarkers in a healthy human skin physiochemical alterations with hereditary and acquired SDs. Thus, it can facilitate designing IHSDMs that may replicate specific in-vivo pathological conditions (Harrer et al. 2019; Davis et al. 2020; Fortino et al. 2020; O’Brien et al. 2021). Since animal disease models (ADMs) lack accuracy for pre-clinical and translational studies posing crucial ethical concerns, the use of highly efficacious IHDSMs offer an attractive solution for thorough investigations related to intricate pathologies of diseases and for the development of effective skin treatment options. While many methods have been explored over the last decade, there is still a lacuna in creating accurate predictive IHSDMs for various human skin types (Liu et al. 2014; Vijayavenkataraman et al. 2016; Wufuer et al. 2016a; Nguyen and Pentoney 2017; Randall et al. 2018; Sarkiri et al. 2019). In this context, biomarkers specific or expressed for certain disease types may become the next generation tools in method development process of IHSDMs.

Table 1.

Healthy skin biomarkers specific to epidermis and dermis regions

Human skin regions	Biomarkers	Function	Reference
Epidermis
Basement membrane	Ki67	Marker of proliferation	Rousselle et al. (2017)
	Hemidesmosomes	Marker of adhesion	Ponec et al. (2000)
Suprabasal keratinocytes	Involucrin	Marker of early differentiation	Poumay and Coquette (2006)
	Keratins 1 and 10,
Granular keratinocytes	Profilaggrin	Marker of terminal differentiation
	Keratohyalin granules
Stratum corneum (SC)	Transglutaminase, Cytokeratin 10	Marker of cornification
SC Lipid barrier	Phospholipids, Cholesterol, sulfate, Glycosphingolipids, Ceramides, Free fatty acids, Cholesterol, Triglycerides, and Cholesterol Esters	Construction of lipid barrier	Kuempel et al. (1998)
Dermal–Epidermal Junction	Laminin-V, Collagen-IV, Collagen-VII, Fibrillin-1	Structural flexibility and integrity	Marionnet et al. (2006a); Bataillon et al. (2019)
Dermis	Fibrillin-1, Pro-collagen-I	Construction of ECM for Dermal skin layer	Bataillon et al. (2019)
	Decorin	Construction of ECM for Dermal skin layer	Krieg and Aumailley (2011b)
	Collagen-VII-A1 (COL-7-A1), Collagen-IV-A1(COL-4-A1)	Fibroblast markers	Supp et al. (2019)

Table 2.

Biomarkers specific to in-vitro human diseased skin models

Human skin diseases	Type of model and skin cells	Biomarkers	Regulation with disease	Description and mechanism	Method of detection	Inducers	Reference
Psoriasis	Organotypic co-culture on a collagen matrix with normal human keratinocytes	Procaspase and Caspase-14	Absent or reduced markers	Loss of keratinocyte terminal differentiation	Immunoblot, Immuno-histochemistry (IHC)	Vitamin D3	Lippens et al. (2004)
		Keratin 1 and 10, Loricrin, and Trans-glutaminase 1
		Ki-67	Upregulated marker	Increase in proliferation
	RHE with normal human keratinocytes	Occludin	Discontinuous expression with enlarged intracellular spaces	Decrease of cellular adhesion in differentiated keratinocytes	Immuno-fluorescence (IF)	IL-17, TNF-α	Chiricozzi et al. (2014)
		C/EBPβ, LCN-2 and HBD2
							Chiricozzi et al. (2014)
	IHSMs with T cells with Th1 and Th17 polarized CD4 + cells	DEFB4, PI3, LCE3A, KRT16 and S100A7	Upregulated	Psoriasis associated genes	qPCR, Immunostaining	anti-CD3/CD28 mAb-coated beads	van den Bogaard et al. (2014)
		Filaggrin, Involucrin	Downregulated	Differentiation markers
		IL23, IL6, IL8, CXCL	Upregulated	Chemokines and pro-inflammatory cytokines increase upon direct and indirect contact from T cells
	HaCaT cell culture	IL28RA	Absent in lesioned tissue	Being a proliferation inhibitor, it disappears in lesioned tissue where excessive proliferation is observed	IHC and western blotting	IL-29	Yin et al. (2019)
Edema	SOC with HaCaT cells, Fbs, HUVECs	IL-1β, IL-6 and IL-8	Upregulated	Inflammatory cytokine	ELISA	TNF-α	Wufuer et al. (2016b)
		Zonula occludens (ZO)	Downregulated	Loss of tight junction protein causes fluid accumulation and swelling	IHC (tight junction staining)
Allergic contact dermatitis	IHSMs with HaCaT and U937 dendritic cell lines	IL-6 and IL-1β	Upregulated	Dendritic cell activation	Sandwich immunoassay	LPS and Nickel Sulfate	Ramadan and Ting (2016)
	EpiCS®, SkinEthic™, EpiDerm™, UVMC-EE	IL-18	Dose dependant upregulation	IL-18 is an inducer of IFN-γ during immune response, thus used to rank potency of sensitizers	ELISA	Contact sensitizers	Gibbs et al. (2013)
	EpiDerm™ with Neonatal foreskin NHEKs	ATF3, IL-8	Not reduced	NrF2 knockdown has no effect	EpiSensA assay: Can identify lipophilic irritants and pre/pro haptens	Contact sensitizers, NrF-2 siRNA	Saito et al. (2017)
		DNAJB4 and GCLM	Downregulated	NrF2 knockdown affects expression
	RHE (Episkin® dermal support with integrated Langerhan cells) with mammary skin NHEKs, CD 34 + derived dendritic cells	IL-1β mRNA	Upregulated for certain sensitizers	Langerhan cell activation	Langerin IHC	TNF-α, IL-1β and other contact irritants, UV irradiation	Facy et al. (2005)
					RT PCR
Atopic dermatitis	RHE	Barrier function	Decreased	Barrier function decreased by cholesterol depletion	TEER with lucifer yellow dye	Methyl-β-cyclodextrin	De Vuyst et al. (2018)
		FLR, LOR	Downregulated markers	Compromised keratinocyte differentiation	qRT-PCR, IHC	IL-4, IL-13, and IL-25
		CA2, NELL2	Upregulated markers	Atopic dermatitis associated genes
	Leiden epidermal model (LEM)	K10, LOR	Decreased expression	Compromised keratinocyte differentiation	IHC, Western blot, RT-qPCR	IL-4, IL-13, or IL-31, TNF-α	Danso et al. (2014)
		Ki-67	Upregulated	Increased basal proliferation except with IL-13
		CerS	Upregulated	Sc lipid profile modification, increase coupled with cholesterol depletion	LC–MS Cer profiling
Vitiligo	RHPE with melanocytes	Melan-A	Downregulated	Melanocyte detachment from basement membrane	Immuno-fluorescence (IF)	IFN-γ and TNF-α	Boukhedouni et al. (2020)
		E-cadherin	Upregulated	Increase in soluble marker levels indicates loss form DEJ
		Caspase-3	Upregulated	Increase in apoptosis	TUNEL assay
Fibrosis	Vascularised IHSMs	ACTA2 mRNA and a-SMA	Upregulated	Fibroblast to myofibroblast transition	qPCR, Immunostaining	TGFβ	Matei et al. (2019)
		PAI1 and SMAD7		TGFb target genes
		COL1A1, COL1A2 and fibronectin mRNA		ECM components generated by fibroblasts
		Collagen 1 protein
Melanoma	Organotypic skin equivalent on Alvetex® with patient derived primary keratinocytes, melanoma cell lines (SK-mel-28, and WM35) and neonatal foreskin fibroblasts	Collagen type IV, Collagen type VII,	Initial intact expression and progressive disruption coupled with metastasis	Initial localization of melanoma cells above collagens IV and VII of the DEJ followed with clear invasion and metastasis into the dermal compartment	IHC	Melanoma cells seeded in the dermal equivalent	Hill et al. (2015)
		Melan-A	Intact expression
	3D organotypic model with SBCL2, WM115, 451-LU melanoma cell lines	Ki-67	seen in peripheral subpopulation of spheroid	Proliferation marker	IHC	Non vascularised spheroids inserted in dermal scaffold	Vörsmann et al. (2013)
		ABCB5 and JARID1B		Chemoresistance proteins
		DNA breaks	Central subpopulation of spheroid	Apoptotic marker	TUNEL assay
Squamous Cell Carcinoma	3D bioprinted model with A431 cSCC keratinocytes	S100A7, S100A8, S100A9, KRT6A, SERPINB3, SERPINB4, and PI3	Upregulated markers	Modulation found to correlate with in vivo levels of said markers and are responsible for over-proliferation, invasion and metastasis	Fluorescence (FL) microscopy using Zs-GFP and tdT-RFP	_	Browning et al. (2020)
		IL-7	Downregulated marker	Blunting of T-cell mediated immune response
	Organotypic co-culture	Collagen IV, laminin, alpha-6 integrin	Disrupted expression	Basement membrane and DJ proteins, disrupted by macrophage invasion	Indirect immuno-fluorescence and zymography	IL-4	Linde et al. (2012)
		F4/80, CD-206, Lvye-1	CD-206 and Lvye-1 detected	Macrophage polarisation markers (M2 polarisation by IL-4)
	IHSMs with SCC 12 cell line derived from head and neck SCC	Cytokeratin-7, E-cadherin, Laminin	Downregulated	Differentiation markers	IHC	_	Obrigkeit et al. (2009)
		Ki-67, MMP-2	Present in invading basal cells	Proliferation and invasion markers
		P-cadherin, Plankophilin 1	Upregulated	SCC specific markers
Hair follicle	3D heterotypic spheroids with HaCaT, DP, and HDF cell lines	BMPs (2,4 and 6), Noggin, Wnt5A	Enhanced expression as compared to 2D culture	Compart-mentalization of DP and dermal cells by keratinocytes	qPCR	_	Tan et al. (2019)
	3D spheroids (hanging drop cultures)	ALP, a-SMA, NCAM	Increased expression in spheroid culture	Hair inductive markers for producing inductive DPC cultures	qRT-PCR, IF, Western blot	_	Lin et al. (2016)
	3D-DP spheroids	FN, SPARC, VCAN	Enhanced expression as compared to 2D culture		RT-PCR, IF	_	Gupta et al. (2018)
	IHSMs	IL-8, IL-1α, and IL-1β	Upregulated after 48 h post infection	Inflammatory cytokines	qPCR, ELISA	A.baumanii strain	de Breij et al. 2012)
Bacterial infections	Labskin-IHSMs	S100 proteins, β-defensin 4A, Elafin, Ubiquitin	Upregulated	Human proteins produced as part of defence mechanism against infection	LESA MS	S. aureus, K. pneumoniae, and P. aeruginosa strains	Havlikova et al. (2020)
		S. aureus: δ-hemolysin, modulin α3		Bacterial proteins that damage epidermal cells
		K. pneumoniae: HU-α
		P. aeruginosa: HU-β
Fungal infections	Episkin®	Hyphae fragments	Detected	Indicates infection of fungus	H&E staining, PAS staining	Trichophyton rubrum, dose of 400 conidia	Liang et al. (2016)
	RHE	Tr 18S rDNA			qPCR, H&E staining,	T. rubrum, Arthroderma benhamiae	Faway et al. (2017)
				Cell viability assay to measure effect of infection on epidermis	MTT assay
Burns	IHSMs			Cell viability assay	MTT assay, RNA staining	Contact burn induction with copper device	Coolen et al. (2008)
		Hsp47	Downregulated in burnt skin	Fibroblast marker that indicates would healing, thus only detected when healing process begins in culture	IHC
		K17, SKALP	Detected in neoepidermis	Epidermal activation and stress markers seen in neoepidermis upon wound healing
		Collagen 4, laminin	Absent in burn wound	Basement membrane proteins
		CD31, α-SMA	Detected	Blood vessel and myofibroblast markers; CD31 detected in wound and a-SMA only in the vessels
Aging	Aged-IHSMs (Senoskin)	SA-β-Gal	Upregulated	A senescence marker, is detected in aged fibroblasts	SA-β-Gal staining	H2O2, senescent fibroblasts	Weinmüllner et al. (2020)
		barrier function	Decreased	Hallmark senescence indicator	Biotin permeability assay
		IL-6, IL-1α, GmCSF	Upregulated	Senescent associated secretory phenotype	ELISA
	IHSMs	Collagen IV/VII, Ki-67, filaggrin, involucrin, loricrin, transglutaminase 1, β-catenin, CD44 (hyaluronan receptor), p16	Downregulated	Parameters of epidermal thickness	IHC		Dos Santos et al. (2015)
Photodegradation	IHSMs	Fibroblasts	_	Apoptosis	TUNEL staining	UVA irradiation	Bernerd and Asselineau (1998)
		LOR	Reduced	Subcellular redistribution due to irradiation	IHC
		MMP1 (Interstitial collagenase)	Increased	indicates dermal damage	ELISA
	Phenion® Full thickness IHSMs	GPX1, GSR,	Upregulation at early recovery	Non enzymatic regulation against ROS formation	qRT-PCR, immuno-fluorescence		Meloni et al. (2010)
		MMP1, MMP9	Upregulation	Specific markers of UVA damage
		Involucrin (IVL)	Upregulation only at early recovery times	Indicator of a adaptive mechanism against UVA by increasing SC thickness, but no increase on cumulative exposure
		IL-10	Dose dependant regulation	Immuno-suppression and photo allergy mediation
		Elastin, Collagen-1, fibrillin-1	Upregulation on acute exposure and downregulation on cumulative exposure	Initial upregulation is an indicator of defence mechanisms, long term downregulation is due to damage
	IHSMs-DED	IL-6, IL-8	Upregulated	Central cytokines mediating local immune responses	ELISA		Fernandez et al. (2014)
		p53	Detected	Suppression of UV induced oncogenesis	IHC
		CPDs	Detected	Specific marker of UVB damage, show immunoreactivity in basal cells
	EpiDerm™ EPI-200	MMP-1, MMP-3, MMP-7, MMP-9	Upregulated	Formation of ROS	qRT-PCR, IHC		Karapetsas et al. (2020)
Particulate matter	SkinEthic™	Lactate dehydrogenase			Colorimetric MMT	PM 0.3–2.5	Verdin et al. (2019)
		IL-1α, IL-8			ELISA
		MMP-1, MMP-3		only 2 out of 1 to 13 MMps modulated	Multiplex panel with Luminex® technology
		4-HNE	Upregulated	lipid peroxidation product	IHC
		HMOX1, MT1G and MT1E		Oxidative stress genes	qRT-PCR

Biomarkers for healthy skin models

Formation of epidermis involves initial proliferation of basal cells and subsequent differentiation and keratinization. The basal layer is composed of cuboidal epithelial cells. As they differentiate into the spinous cells, the anchoring of cells via hemidesmosomes increases (Ponec et al. 2000). This differentiation is accompanied by expression of biomarkers such as keratins 1 and 10, transglutaminases, and involucrin. Meanwhile, the granulosa cells are characterized by markers, such as loricrin, profilaggrin, and cystatin, that are secreted by the keratohyalin granules in the KCs. The transition of living granulosa cells into dead cornified stratum corneum (SC) is an important step in their differentiation and is accompanied with high activity of enzymes such as transglutaminase and filaggrin (Poumay and Coquette 2006).

The interior of corneocytes is packed with filaments of keratin, embedded in a matrix that principally consists of filaggrin. The major constituents of the intercellular structure of the SC include lipids such as ceramides, FFA (free-fatty acids), cholesterol, and other minor constituents like cholesterol sulfate and proteolipids (Kuempel et al. 1998). These non-polar lipids are deposited in the form of lamellar bilayers and are responsible for the barrier function of the SC. Cholesterol is an essential component of this layer and is known to enhance the stability of the lipid layer. The adhesion and desquamation SC is regulated by intercellular hydrolytic enzymes, including the ones that maintain the ratio of cholesterol and cholesterol sulfate (Schurer and Elias 1991).

The dermal-epidermal junctions (DEJ) are responsible for cohesion between dermis and epidermis. It is further subdivided into the stratum basale (SB), composed of basal KCs and hemidemosomes; lamina-lucida; lamina-densa; and sublamina-densa, connected to dermis. Several membrane proteins such as collagen-IV/VII, procollagens-I/III, and fibrillin-I, are deposited in this region. The DEJ has also been studied by IHSMs and various proteins including collagens-IV/VII, laminin-10/11, and nidogen have been identified in the IHSMs using quantitative RT-PCR, immunostaining, and transmission-electron microscopy (Marionnet et al. 2006b). The dermis contains FBs, extracellular matrix (ECM) components, and the roots of hair follicles. The ECM is composed of collagens-I/III/V and contains matrix-metalloproteinases (MMPs) that remodel the collagenous networks present there. Decorin, which is known to stabilize the interactions between collagen fibers, is a common component of connective tissues and hence abundant in the dermis (Krieg and Aumailley 2011a). Table 1 presents an overview of the healthy skin biomarkers and their major functions in the respective skin layers.

Even for the diseases associated with a particular region, e.g. epidermis in case of atopic dermatitis, markers of other layers e.g. dermis are taken into consideration (Randall et al. 2018; Sarkiri et al. 2019). They may help in the discovery of new signalling mechanisms that elucidate certain unexplained aspects of the disease physiology or can be exploited to generate therapeutic antibodies or compounds. Developing much accurate IHSMs using biomarkers (Table 1), will have a huge application in cosmetics and pharmaceutical sectors as alternative-to-animal models. IHSMs can be used for safety evaluations of drugs/medicines/compounds and extracts for personalized and early treatment of specific skin diseases. Also, IHSMs are suitable for studying the diffusion, permeability, toxicity, irritancy/corrosivity, and sensitivity caused by these ingredients (Costin and Norman 2017; Hubaux et al. 2018).

Biomarkers in diseased skin models

As per the report of the World Health Organisation (WHO) published in 2021, skin associated disorders and diseases are the fourth most highly prevalent type of human disease in the world, affecting nearly about 149 million individuals or one-third of the total population, at any given time (Flohr and Hay 2021). These range from a minor itch of dermatitis to notorious conditions like skin cancers and vitiligo. The global prevalence rate (percentage) of inflammatory skin disorders is 15–20% in children and 1–3% in adults. Also, 1 to 10 individuals out of 1,00,000 people are affected by autoimmune skin conditions such as psoriasis, atopic dermatitis, scleroderma and pemphigus (Sticherling and Erfurt-Berge 2012). Additionally, over the past few decades, skin cancer cases have been rising by 1.2% with the incidence of approximately 99,780 new melanoma cases in 2022 (Sung et al. 2021, https://seer.cancer.gov/statfacts/html/melan.html). Skin allergy and hair loss are also very prevalent disorders in a population that has a significant impact on daily lifestyle (Pratt et al. 2017; Zuberbier et al. 2018; Papadopoulos 2020; Wedi and Traidl 2021). Thus, IHDSMs are important for developing therapies and understanding the physiology and trigger mechanisms for these disorders (Carlson et al. 2008). Particularly, testing of potential cosmetics and drug molecules using these models is an extremely desirable alternative to animal-based disease modeling and/or drug efficacy testing, not only for ethical reasons but also because the IHSMs can provide structural and physiological similarities to human skin (Hewitt et al. 2013). For inflammatory conditions like psoriasis and dermatitis, few models are being developed employing T-cells. Nevertheless, more molecular interactions need to be studied and quantified to identify the key markers that may evaluate the success of IHDSMs (Barker et al. 2004; Donetti et al. 2014; De Vuyst et al. 2017; Hubaux et al. 2018). For allergic dermatitis, potential contact allergens are evaluated (Peiser et al. 2012). For burn victims, graft generation, and skin infections, different skin layered in-vitro models are being employed for pathogen identification and the development of newer treatment approaches (Boyce et al. 1999; Liang et al. 2016; Faway et al. 2017; Kim et al. 2019).

Inflammatory skin conditions

Psoriasis

Psoriasis is a complex systemic and autoimmune disease with a global prevalence rate of 2–3% i.e., 100 million individuals in a population (Damiani et al. 2021). It is marked by uncontrolled proliferation of KCs, coupled with dysfunctional differentiation. There is also infiltration of immune cells as a result of the faulty local inflammatory response, where cytokines ramp up autoinflammation (Rendon and Schäkel 2019). The pathogenesis of psoriasis is also dependent upon numerous inflammatory cytokines and chemokines. KCs express cytokine markers such as IL-17 (Interleukin-17). IL-17 is produced by the memory T-cells and natural killer cells (NK); expressing its receptors on epithelial cells, fibroblasts, B and T-lymphocytes, and monocytes. IL-17 not only acts as a cytokine but also as an inducer of several inflammatory genes in differentiated KCs (Chiricozzi et al. 2014). TNF-α is another proapoptotic molecule that stimulates the production of chemokines. In RHE static models, short-term exposure to IL-17 and TNF-α resulted in the enlargement of intercellular spaces in the spinous layer and the epidermal-dermal junction, as observed after H and E staining. Additionally, immunohistochemical (IHC) analysis revealed discontinuous expression of occludin, a cell adhesion marker (Donetti et al. 2014). A separate study analyzing the effect of IL-17 in RHE revealed its role in the regulation of over 600 genes via the C/EBPβ transcription factor (Chiricozzi et al. 2014). Also, IL-28RA is also associated with the pathogenesis of psoriasis, as seen in genome-wide association studies. It is being studied as a potential drug target for the treatment of psoriasis since it acts as a cell cycle progression inhibitor. In-vitro studies also have shown that when IL-28RA is overexpressed in 2D human KC cells, excessive cell proliferation is inhibited (Yin et al. 2019). Thus, this disorder may be recapitulated in-vitro by using biomarkers as summarized in Table 2.

Oedema

Induction of oedema using TNF-α has been conducted in a SOC microfluidic model consisting of epidermal, dermal, and vascular layers that recapitulate the dynamic skin microenvironment. TNF-α is induced in the tight junctions of the epidermis. Evaluation of this model with immunocytochemical (ICC) staining and ELISA revealed a direct dose-dependent increase in inflammatory cytokine markers such as IL-1β, IL-6, and IL-8 after treatment with TNF-α (Wufuer et al. 2016a). Thus, these biomarkers maybe suitable for evaluating in-vitro skin inflammation models.

Dermatitis

Allergic contact dermatitis

Allergic contact dermatitis is a form of T-cell mediated delayed immune reaction caused by chemical allergens. An allergic reaction has two phases, namely an initial sensitization to an allergen and elicitation upon re-exposure. The initial sensitization occurs via three key events (Peiser et al. 2012), namely the penetration of haptens through KCs, followed by KCs activation and release of cytokines that attract dendritic cells (DCs). The final key event occurs when sensitized langerhan cells (LCs) migrate to the dermis as they mature, and eventually enter the lymph nodes. Here, they present the antigens to T-cells which, upon elicitation, trigger the immune response. MatTek EpiDerm™, epiCS®, SkinEthic™ have been specifically used to investigate the first two events. Activation of KCs in these models results in the secretion of IL-1 α, IL-1β, TNF-α, and IL-18. Out of these, IL-18 is used as a robust biomarker to identify allergens and to assess the contact sensitization potency (Gibbs et al. 2013).

Cellular stress genes have also been explored as useful biomarkers, especially during the sensitization assays conducted using LabCyte EPI-MODEL and EpiDerm™. In these models, cellular stress-associated genes, such as ATF3, DNAJB4, and GCLM, etc., exhibited a high expression, when studied in response to the treatment with sixteen different European Centre for Validation of Alternative Methods (ECVAM) reference chemicals i.e., twelve sensitizers and four non-sensitizers (Saito et al. 2017). Also, the latest immune-competent models include LCs or T-cells and measure the reactivity of LCs to topical allergens. D34 + derived DCs and epidermal cells seeded onto EpiSkin™ support predominantly produce IL-1β and CD86 mRNA as the disease markers. Using various contact irritants, UV-radiation and inflammatory cytokines including TNF-α and IL-β1, the levels of these markers were found to be elevated. Thus, these molecules may act as convenient biomarkers while modeling skin dermatitis (Facy et al. 2005). Therefore, patient specific ‘allergy IHSMs’ may be designed using these biomarkers and be subsequently employed for testing potent allergens, which may help in development of personalised medications.

Atopic dermatitis

Atopic dermatitis (AD) results in the loss of barrier function of the skin. Its prevalence is around 2.4% worldwide with the incident rate of new cases being approximately 17.1% for adults and 22.6% for children every year (Bylund et al. 2020). In 3D models, often the genes associated with the barrier function are silenced to induce disease. TH2 cytokines are especially important, as they are the primary cytokines released when the barrier function is disrupted to allow penetration of irritants (Danso et al. 2014). Amongst these, IL-4 and IL-13 exhibited an elevated expression in the diseased skin (Tazawa et al. 2004). A combination of both these inducers has been explored in vitro models, wherein the ILs were introduced in a model having an impaired barrier function, that was created via the removal of cholesterol from the epidermis. Such models have exhibited changes in the expression of AD-specific biomarkers such as filaggrin (FLG), loricrin (LOR), and hyaluronic acid synthase 3 (HAS3) (De Vuyst et al. 2018). Thus, these related biomarkers maybe ideal for evaluating such autoimmune conditions, thereby facilitating the development of 3D skin dermatitis models.

Vitiligo

Vitiligo is a chronic skin depigmentation disease that arises due to the selective depletion of melanocytes. Various underlying reasons are responsible for causing melanocyte deficiency in vitiligo (Chen et al. 2021). It is thus considered a multifactorial disease, wherein both genetic and environmental factors are involved in the disease's onset and progression. It occurs in 0.5—2% of the population worldwide (Bergqvist and Ezzedine 2020). The presence of intrinsic melanocyte anomalies contributes toward compromised melanocyte degeneration and/or proliferation, which confirmed the theory that the condition was primarily caused due to melanocyte deficiency (Cario-André et al. 2018). Figure 1 shows a schematic description of one such example.

Fig. 1.

a In-vitro modeling of the reconstructed human pigmented epidermis (RHPE) using dead deepidermized dermis (DDD), b Double staining melanA (red) and c E-cadherin (green) on RHPE.

Adapted from Cario-André et al. 2018 (Cario-André et al. 2018)

Melanocyte detachment is an important aspect of the pathogenesis of vitiligo, which occurs due to the apoptotic loss of melanocytes. RHE containing melanocytes was used for this investigation. This model confirmed the relation between the release of inflammatory cytokines with the detachment and subsequent loss of melanocytes. Here, the primary immune cells involved in disease induction were Th1/Tc1 cells, and the cytokines associated with them were IFN-γ and TNF-α. When used in combination as disease inducers, IFN-γ and TNF-α stimulated the release of matrix metalloproteinases (MMPs), such as MMP-9 to cleave the melanocyte adhesion protein, E-cadherin (Boukhedouni et al. 2020). In RHPE, this soluble E-cadherin was detected by LAMP staining. IFN-γ is also known to promote apoptosis of melanocytes, which causes and propagates vitiligo lesions. Although, introduction of IFN-γ in human melanocyte cultures resulted in an increase in the apoptotic markers, such as caspase-3 (Su et al. 2020), the same effect was not observed in RHPE but was confirmed by MTT and TUNEL staining (Ahn et al. 2015; Boukhedouni et al. 2020). Reconstruction of vitiligo skin models by incorporating described biomarkers may be applied for further preclinical studies of potential pharmaceutical compounds.

Skin fibrosis

Dermal FBs are spatially classified by papillary (upper) and reticular (lower) dermal FBs. Amongst these, the reticular FBs are primarily involved in wound healing. Attempts have been made to involve the papillary dermis in the wound healing process as this could potentially prevent scarring and allow regeneration of the skin appendages (Woodley 2017).

Epithelial injuries induce differentiation of dermal FBs into myofibroblasts, which start secreting matrix components such as collagen-I, collagen-II, and fibronectin, to aid in tissue healing. This phenomenon is termed ‘fibroblast plasticity’. If this myofibroblast differentiation persists, it can lead to excessive matrix deposition, especially of collagen, and can cause chronic fibrotic disorders, such as scleroderma (Garrett et al. 2017), Transforming growth factor-β (TGF-β) mediates the conversion of FBs and endothelial cells into myofibroblasts. This was seen in a vascularized IHSMs. The increased conversion was observed through the qPCR analysis of ACTA2 mRNA. PAI1 and SMAD7 (TGF-β receptors) are involved in the TGF-β mediated pathway (as shown in Fig. 2), which leads to increased levels of dermal markers such as collagen-I, mRNA of COL1A1, COL1A2, and fibronectin mRNA (Alsharabasy and Pandit 2021).

Fig. 2.

(1) Myofibroblasts: its origin and function in the skin fibrosis, (2) In-vitro modeling of skin fibrosis,

(Adapted from Alsharabasy A et al., 2021; under CC BY 4.0) (Alsharabasy and Pandit 2021)

Signalling between FBs, endothelial cells of the vasculature and KCs produces pro-fibrotic cytokines, such as IL-1α, IL-6, oncostatin-M and vascular endothelial growth factor (VEGF) (Matei et al. 2019). Several specific biomarkers for constructing in-vitro skin fibrosis models have been summarised in Table 2, which can aid in development of personalised drugs and therapeutics.

Skin cancer

Melanoma

Malignant melanoma is an extremely aggressive form of cancer that has shown an increasingly growing incidence. It accounts for 1% of all skin cancer types but is associated with the highest number of skin cancer-related deaths (https://www.cancer.org/cancer/melanoma-skin-cancer/about/key-statistics.html) (Eddy et al. 2021). Melanoma progression occurs in two phases, viz. the radial growth phase (RGP) in which the melanoma spreads horizontally on the epidermis, followed by the vertical growth phase (VGP), in which the tumour grows vertically in the skin, resulting in the migration and invasion of metastatic cells from the epidermis to the dermis (Eddy et al. 2021).

Many methods of fabricating organotypic melanoma models have been discussed, however, the one that ensures the introduction of melanoma cells in their natural environment can best represent the tumour progression. This method involves the seeding of melanoma cells on the dermis, before the addition of KCs. IHC investigations performed using this model primarily revealed the destruction of the ECM component markers, such as collagens, cytokeratins (CKs), and involucrin. The emergence of metastatic melanoma nests was also detected by the Melan-A marker (Hill et al. 2015). Another study involving IHSMs was conducted with specific RGP and VGP cell lines. Here, non-vascularised tumour spheroids were generated by the hanging drop culture, and these were incorporated into the IHSMs. The rationale was to generate a tumour that was comparable in size to in-vivo tumours, which was not possible in the case of tumors developed after seeding cells in IHSMs. The invasiveness of melanoma was observed only with nests of VGP cells. This study differentiated between the peripheral and central subpopulations of cells via IHC evaluation of markers. While the peripheral subpopulations displayed high proliferation, as seen by the increased Ki67 levels, the central subpopulations were highly apoptotic, forming a necrotic spot, as shown by TUNEL staining (Vörsmann et al. 2013).

Squamous cell carcinoma

Non-melanoma skin cancer or cutaneous squamous cell carcinoma (SCC) cause more than 1 million death globally. It constitute 25% of total of non-melanoma skin cancer cases (Ciuciulete et al. 2022). It is characterized by KCs turning malignant. When this disease was induced in IHSMs, tumor cells started invading and exhibited a high expression of MMPs. This lead to alterations in various molecular and functional aspects of skin, such as proliferation, differentiation, keratin expression, and apoptosis, which were assessed via IHC (McCawley et al. 2008). The effect of carcinoma in reducing proliferation was noted with a decrease in epidermal involucrin and cytokeratin-7 levels (Pandey et al. 2021). Five days post-infection, E-cadherin was completely lost, while Ki67 levels were increased, indicating an enhanced mitotic activity associated with SCC proliferation (Dumitru et al. 2022). The most novel markers were the SCC-specific markers, such as P-cadherin and plankophilin, which appeared in a clustered manner around the SCC tumors (Obrigkeit et al. 2009).

Apart from FBs and KCs, tumor cells also interact with inflammatory cells, such as macrophages, that produce cytokines, angiogenic factors, etc. Macrophages undergo polarization (M1 or M2) and thereafter produce cytokines. Markers for M1 and M2 polarized macrophages are iNOS and CD-206, respectively, which have been detected using immunofluorescence (IF) techniques (Linde et al. 2012). Amongst various cytokines, IL-6 stimulates the production of various autocrine and paracrine cytokines during tumorigenesis. It also promotes the progression of benign tumors into malignant and invasive ones by increasing proliferation. IL-6 mediated stimulation in HaCaT cultures resulted in a marked increase of angiogenic factors, such as VEGF (Lederle et al. 2011). Monocytes isolated from patients having head and neck SCC and co-cultured into spheroids also expressed monocyte chemoattractant protein-1 (MCP-1), which promoted monocytes and macrophages to secrete IL-6 and acted as an immune response inhibiting cytokine. MCP-1 is of great importance as it is also associated with macrophage infiltration into the tumorous tissue, which is characteristic of the prognosis of SCC (Kross et al. 2008). Table 2 summarizes various skin cancer models along with biomarkers and their role in course of cancer onset. Thus, cancer induced IHSMs is possible with the identification of relevant biomarkers specific to disease progression in healthy skin. Additionally, these models can supplement in faster introduction of novel drug molecules in pharmaceutical market as well as for early treatment options for cancer patients.

In-vitro hair follicles

Human skin consists of regularly interspaced hair follicles, which despite being primarily epidermal structures, are defined by the nature of the dermal environment necessary for their establishment. A typical hair follicle bulb consists of regions such as the dermal papilla (DP), which is composed of epidermal matrix cells and stem cells; the hair root, hair shaft, and the connective tissue layer (Mou et al. 2006). The DP cells (DPC) are a type of mesenchymal cell and during follicle development, they instruct the matrix cells to differentiate and form multiple layers of the hair shaft (Driskell et al. 2011). Each hair follicle perpetually goes through three stages, viz. the growth (anagen), involution (catagen), and resting (telogen) stage (Paus and Cotsarelis 2008).

Various signalling mechanisms have been identified in the mesenchymal-epidermal crosstalk that occurs during the follicle generation process, including the Wnt-β/β-catenin pathway and the noggin/BMP signalling pathway. Wnt (Wingless-type mouse mammary tumour virus integration sites) are signalling molecules that bind to their receptors and cause translocation of cytoplasmic β-catenin into the cellular nucleus, which is necessary for the formation of DP condensates during follicle development (Andl et al. 2002). In 3D heterotypic spheroids were generated using a combination of HaCaT, DPC, and FBs. Upon qPCR analysis, these spheroids exhibited the presence of various BMP proteins, noggin, and Wnt5A markers. While simple DP spheroids exhibited nominal levels of these markers, they displayed an appreciable level of ECM markers like VSCAN, fibronectin (Gupta et al. 2018). Figure 3 shows the development of artificial hair follicles in-vitro in the 3D human skin culture by using DPCs. It was also observed that DPs lost their follicle generation capability in-vitro, and thus a hanging drop spheroids were developed, which successfully presented all the markers relating to follicle inductivity, such as ALP, α-SMA, and NCAM (Lin et al. 2016; Abaci et al. 2018).

Fig. 3.

In vitro modelling of hair follicle (HF) in IHSMs. a 3D-printed molds for creating microwells b floating on collagen gel containing FBs to prepare HF-like extensions (4 mm deep cavity) and DPC are seeded onto the groove of created microwells c Schematic view of IHSMs and cell type arrangements d Cytokeratin-14-positive KCs proliferating in the grooves, e H&E-stained cross-sections showing morphological changes of KCs with DPC in groove h but not in DPC absent group, g white arrows indicating hair fiber formed and protruding from the IHSMs. (Adapted from Abaci, H.E. et al. 2018; under CC BY 4.0) (Abaci et al. 2018)

Microbial infections

Bacterial infections

Hospital-acquired or opportunistic infections are one of the major causes of complications of pre-existing ailments such as diabetic foot ulcer, HIV, wounds etc. The ESKAPE group of pathogens, including the Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. etc. are the primary causative agents for hospital-acquired infections (Pendleton et al. 2013). Several human and bacterial proteins have been identified as markers of infections. For instance, S. aureus produces two toxins, namely modulin α3 and δ-hemolysin, which are cytotoxic phenol soluble modulin proteins (Havlikova et al. 2020).

Among the human proteins, ß defensins are extremely important. These constitute a part of the tissue-specific defense response of the body and belong to the broader class of defensin peptides. They are also being researched as therapeutic agents for the treatment of infected wounds, especially ß defensin-3 (Gibson et al. 2012). Further, studies with skin models infected with Acinetobacter baumannii have revealed IL-1α as a biomarker due to an elevation in its concentration upon exposure of the skin models to this bacterium. It was also noted that the bacterium did not penetrate the skin but stayed within the SC, as the levels of proliferative markers Ki67, K10, and K16 remained normal. Furthermore, skin equivalents have also expressed the antimicrobial defensin peptides hBD-2 and hBD-3 (de Breij et al. 2012).

Fungal infections

Dermatophytosis or fungal infection of the skin is commonly caused by the organism Trichophyton rubrum. Upon infection, necrosis was observed, along with hyphae and conidia in the SC layer (Samanta 2015). Disease models depicting in-vivo infection have been replicated using commercial in-vitro skin models, such as EpiSkin™ and EpiDerm™. While the presence of hyphae is a physical marker of disease progression, the release of cytoplasmic lactate dehydrogenase from the KCs is an important biomarker for dermatophytosis. EpiDerm™ revealed that cytoplasmic lactate dehydrogenase didn’t indicate location of the fungal infection, utilisation of H and E staining and Periodic acid-Schiff (PAS) obviated this limitation (Liang et al. 2016).

Burns

Skin burns are classified based on their nature and severity. Accordingly, these have been classified as contact burns or flame burns. Further, based on their severity they are classified as first, second-, third- and fourth-degree burns. Burns cause a wide range of damages, ranging from slowly expanding damage to subcutaneous persistent damage (Warby and Maani 2022). In-vitro studies of burn-related skin damage can be conducted in several ways. Ex-vivo skin tissue, when subject to heat stimuli, develops burn wounds and can be maintained in culture for several days to observe the differences in re-epithelialization of burn damage versus healthy skin (Gross-Amat et al. 2020). Primary IHC analysis was conducted for ECM, myofibroblasts, and stress markers. Amongst the ECM markers, collagen-IV and laminin of the DEJ were heavily downregulated, with a complete absence of collagen-IV and only a residual presence of laminin (Tracy et al. 2016). Epidermal differentiation was measured based on the SKALP marker, which is an important marker in epidermal wound healing. This marker was detected in the burn model while being absent in the unburnt tissue (Coolen et al. 2008). Additionally, in-vitro analysis was performed in composite tissue models to evaluate the potential differences in tissue with burns of different dynamics, such as contact and flame burns of varying severity. Irreversible dermal collagen destruction was observed in case of both kinds of burns. An interesting difference was in the subcutaneous damage of adipocytes, wherein contact burns showed continuous expansion of subcutaneous damage, as opposed to flame burns (Hao and Nourbakhsh 2021). Again, both types of burns demonstrated an increase in CD80, CD163, and HSP47 stained cells after IHC, indicating macrophage activation (Hao et al. 2021).

Aging/Senescence

Senescence is defined as the irreversible arrest of cell proliferation and is caused due to a myriad of factors. It results in a decrease in the functional capacity of tissues. The genetic cause of senescence is associated with the DNA replication process, which is an important step in cell division and proliferation (Kumari and Jat 2021). Senescent cells are known to exhibit increased lysosomal senescence-associated β-galactosidase (SA-β-gal) activity (Dreesen and Wang 2018). A study of cellular aging was conducted in 3D models by seeding increased proportions of SIPS (stress-induced premature senescent) fibroblast cells. Such FBs were identified by their increased SA-β-gal activity. This model termed ‘senoskin’ showed many prominent hallmarks of aging, including a decrease in epidermal thickness. This indicated a shift in the proliferative and differentiating balance of the epidermis, which was confirmed by IHC analysis of Ki67 and filaggrin respectively. Increased levels of SASP (senescent associated secretory phenotype) markers, namely IL-6 and GmCSF, which affect the phenotypic changes, were also noted (Weinmüllner et al. 2020).

As per global estimation by 2050, ~ 1.5 billion population are estimated to attend 65 years of age or older (Sinikumpu et al. 2020). So these models not only have a huge commercial potential for studying more human skin related intrinsic and extrinsic pathophysiological aging mechanisms and testing pharmaceutical and cosmetic ingredients but also for treatment of different aging associated skin disorders such as xerosis cutis, atrophy, progeria, dyspigmentation, wrinkles, etc. (Duval et al. 2014; Dos Santos et al. 2015; Blume-Peytavi et al. 2016; Lim et al. 2018; Bataillon et al. 2019; Weinmüllner et al. 2020).

External environmental factors (UV exposure/pollution/dust)

Ultraviolet (UV) radiation exposure

Repeated exposure to the sun is known to cause skin damage, resulting in sunburn, or long-term effects, such as photoaging and ultraviolet (UV)-induced skin cancer, namely basal cell carcinoma (BCC) which accounts for 70% of the non-melanoma skin cancer cases (Ciuciulete et al. 2022). These UV-induced damages affect both epidermis and dermal skin, and are directly related to the biological effects of both UVB (290–320 nm) and UVA (320–400 nm) radiations (Wang et al. 2019).

The primary effect of UVA exposure is seen in the dermal region due to its higher penetration properties. FBs are more prone to damage by oxidative stress followed by apoptosis than KCs (Bernerd et al. 2022). Its exposure primarily results in the production of reactive oxygen species, which causes inflammation, pigmentation, DNA damage over the short term, and photoaging and elastosis in the long run (Gromkowska‐Kępka et al. 2021). The major marker of UVA damage is MMP-1, which is an interstitial collagenase enzyme produced by FBs (Bernerd and Asselineau 1998). MMP-1 causes hydrolysis of collagen-I, which is the major component of the dermal ECM. This ECM markers, like fibrillin and elastin, were also downregulated in UVA exposed cancer IHSMs. Along with MMP-1, gene expression of enzymes such as superoxide dismutase-2 and home oxygenase-1 was also observed in skin equivalent models (Battie et al. 2014).

Histologically, UVB damage manifests in the form of ‘sunburn cells’, which are characterized by condensed nuclei, eosinophilic cytoplasm and supra-basal localization in the epidermis (Raj et al. 2006). The notable UVB-mediated damage signs were induction of DNA damage, caused by dimer formation between pyrimidines. Primarily, they included molecular lesions in the form of CPDs (Cyclobutene pyrimidine dimers) (You et al. 2001). Furthermore, overexpression of matrix metalloproteinases also occurred in KCs, which was studied in an EpiDerm™ (Bernerd and Asselineau 2008; Karapetsas et al. 2020). Even so, these markers gradually fade with time due to the regenerative capacity of KCs, in-vitro models have shown increased Ki67 expression in basal layer as it facilitates regeneration. There is also an upregulation in IL-6 and IL-8 due to the activation of a local immune response, as well as increased levels of keratins 1, 10, and 11 in the spinous and granular layers due to increased differentiation (Fernandez et al. 2014).

Particulate matter and pollutants

Particulate matter (PM) may include organic compounds, inorganic carbon, sulfates, nitrates, etc. The primary effect of PM 0.3–2.5 µm on RHE was oxidative stress and lipid peroxidation (Hu et al. 2017), which impacted the dermal ECM. MMP-1 and MMP-3 levels were modulated and there was a distinct decrease in the epidermal marker loricrin. Also, increased production of cytokines like IL-1α and IL-8 after exposure to PM 2.5 µm particularly triggered apoptosis in KCs, due to which caspase-3, caspase-9, and cytochrome-c were overexpressed (Verdin et al. 2019).

Conclusion and future aspects

The advent of IHSMs has allowed for increased accuracy in the way that skin morphology and physiology are represented. A variety of physiological processes like proliferation, differentiation, follicle generation, and senescence can be investigated using IHSMs, based on biomarkers indicative of these processes. Additionally, IHSMs have exhibited promise for studying diseases such as psoriasis, dermatitis, melanomas, vitiligo, and wound healing. However, scientists are yet to develop IHSDMs for BCC which is least aggressive but most common skin cancer type caused by different aetiology (for example, long exposure to UV radiations as explained in Sect. 11.1.). It is characterised by distinct biomarkers such as epithelial adhesion molecule (EpCAM)/ Br-EP4 (antibody to EpCAM), transglutaminase-3 (TGM3), α-smooth muscle actin (α-SMA) etc. (Grando et al. 1996; Gore et al. 2006; Rahman 2013; Dasgeb et al. 2013; Smirnov et al. 2019; Liu et al. 2020). Bioengineered skin (BS) has also been used as grafts in cases of skin losses, such as severe burns. Analysis of IHSMs/IHSDMs requires the identification and quantification of biomarkers. In healthy IHSMs, they are indicative of physiological functions and help in determining if a model accurately mimics human skin. However, in IHSDMs, the entire process of disease induction, development, and subsequent testing of novel therapies depend on qualitative and quantitative analysis of biomarkers. Currently, models are mostly developed by a layer-by-layer co-culture method. This involves the seeding of keratinocytes upon dermal layers containing fibroblasts, and exposure to an air–liquid interface to allow stratification of the epidermal layer. BS was originally developed for pre-clinical testing of cosmetics and pharmaceutical products (Suhail et al. 2019). Transplantation therapy using in-vitro artificially synthesized skin tissue in cases of serious injuries, such as burn wounds or chronic diabetic wounds, is also extensively studied and implemented. Its use is now moving towards regenerative medicine and it could be potentially used to treat genetic skin disorders when used in combination with gene therapy. Currently, the primary limitation of IHSDMs is the lack of standardization and time-intensive protocols. The ability to regenerate human skin with genetically modified cells or iPSCs allows for the exploration of innovative therapies and can also aid in discovering signalling pathways and associated markers for dermatological conditions.

Proteomic studies, such as liquid chromatography-tandem mass spectrometry (LC–MS/MS), of healthy and diseased skin can be employed for quantification and characterization of radial and specific molecular biomarkers or proteomes (Fredman et al. 2021). These techniques will aid in the development of more precise and personalized IHSMs/IHSDMs in less time, thus help in drug discovery and choosing potential therapeutic options. R and D efforts are being undertaken across the globe to achieve robotic human skin emulation and pre-clinical SOC technology, which may be integrated with artificial intelligence-based software tools for automated handling, efficiently analyzing individual-specific cellular, proteomic, and genomic status/profile of the human skin. This may also allow quick comparison and recording of the statistical relevant data on cloud-web interfaces and ultimately enable the prediction of the pros and cons of newer topical compounds based on real-time standards. Additionally, artificial intelligence may also help in the designing of diseased artificial organs paving the way to efficient treatment and surgery.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

FLG

Filaggrin

LOR

Loricrin

CA2

Carbonic anhydrase 2

NELL2

Neural epidermal growth factor like2

IHC

Immunohistochemical staining

TSLP

Thymic stromal lymphopoeitin

RHPE

Reconstructed human pigmented epidermis

TGFβ

Transforming growth factor β

MCP-1

Monocyte chemo-attractant protein

SPARC

Secreted protein acidic and rich in cysteine

LESA MS

Liquid extraction surface analysis MS

H and E

Haematoxylin and Eosin staining

PAS

Periodic acid-Schiff staining

COL1A1

Collagen type I A1

COL7A1

Collagen type VII A1

CCND1

Cyclin D1

DCN

Decorin

ELN

Elastin

FBN1

Fibrillin 1

GPX1

Glutathione peroxidase 1

GSR

Glutathione reductase

IL1α

Interleukin 1α

IL10

Interleukin 10

ITGβ1

Integrin β1

IVL

Involucrin

MMP1

Matrix metalloproteinase 1

MMP9

Matrix metalloproteinase-9

CPDs

Cyclopyrimidine dimers

4-HNE

4-Hydroxynonenal

GmCSF

Granulocyte macrophage colony-stimulating factor

α-SMA

Alpha smooth muscle actin

ECML

Extra Cellular Matrix

FFAs

Free Fatty Acids

SCC

Squamous Cell Carcinoma

MMPs

Matrix Metalloproteinases

DEJ

Dermal Epidermal Junction

IL

Interleukin

TNF

Tumor Necrosis Factor

IFN

Interferon

MGP

Matrix Gla Protein

VEFG

Vascular endothelial growth factor

MCP

Monocyte chemoattractant protein

CPD

Cyclo-pyrimidine dimers

DP

Dermal Papilla

ALP

Alkaline Phosphatase

NCAM

Neural cell adhesion molecule

SMA

Smooth Muscle antibody

BMP

Bone morphogenetic protein

PAS

Periodic acid-Schiff

MTT

3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium Bromide

C/EBPβ

C/CAAT-enhancer-binding proteins

Authors contribution

Literature survey, data analysis, study design, writing—draft preparation: DC, SP; Supervision: PD, RJ; Writing—reviewing and editing: PD.

Funding

D.C. is financially supported by Department of Biotechnology (DBT-ATGC) and Department of Science and Technology (DST-PURSE), Government of India.

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current review article.

Declarations

Conflict of interests

The authors have no relevant financial and non-financial interests to disclose.

Ethical Approval

"Not Applicable".

Informed consent

"Not Applicable".