Applications of Omics Technologies for Three-Dimensional In Vitro Disease Models

Abstract

Omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, multiomics, and integrated modalities, have greatly contributed to our understanding of various diseases by enabling researchers to probe the molecular wiring of cellular systems in a high-throughput and precise manner. With the development of tissue-engineered three-dimensional (3D) in vitro disease models, such as organoids and spheroids, there is potential of integrating omics technologies with 3D disease models to elucidate the complex links between genotype and phenotype. These 3D disease models have been used to model cancer, infectious disease, toxicity, neurological disorders, and others. In this review, we provide an overview of omics technologies, highlight current and emerging studies, discuss the associated experimental design considerations, barriers and challenges of omics technologies, and provide an outlook on the future applications of omics technologies with 3D models. Overall, this review aims to provide a valuable resource for tissue engineers seeking to leverage omics technologies for diving deeper into biological discovery.

Impact statement

With the emergence of three-dimensional (3D) in vitro disease models, tissue engineers are increasingly interested to investigate these systems to address biological questions related to disease mechanism, drug target discovery, therapy resistance, and more. Omics technologies are a powerful and high-throughput approach, but their application for 3D disease models is not maximally utilized. This review illustrates the achievements and potential of using omics technologies to leverage the full potential of 3D in vitro disease models. This will improve the quality of such models, advance our understanding of disease, and contribute to therapy development.

Introduction

Over the last decade, omics technologies, namely genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, multiomics, and integrated modalities, have helped unmask the molecular wiring of cellular systems in a high-throughput and precise manner. Early applications of these technologies have focused extensively on precision medicine applications by investigating patient samples for disease diagnosis and biomarker identification.^1–4 These omics technologies also offer the opportunity to molecularly dissect disease mechanisms and identify novel targets. For these mechanistic applications, it is not typically possible to probe specific questions to link genotype to phenotype for distinct cell populations in the tissue, and it is challenging to perform spatiotemporally resolved omics to understand heterogeneity and microenvironmental impact. Furthermore, the use of patient samples is not always ideal as these are not widely available for all diseases. Recently, three-dimensional (3D) in vitro disease models, such as patient-derived organoids (PDOs) and spheroids, have emerged as valuable engineered constructs that can recapitulate the in vivo microenvironment, but can be more easily manipulated to decipher the relationship between genotype and phenotype. While 3D in vitro models have also been used to study tissue development, in this study, we focus solely on disease modeling. In this review, we provide an overview of existing omics technologies, discuss the current and emerging applications of omics technologies with 3D disease models, and highlight the experimental design considerations, barriers, and challenges of applying omics to 3D disease models. The goal of this review is to provide a resource for tissue engineers seeking to leverage omics technologies for biological discovery, and to provide an outlook on the future of omics and 3D disease models in unraveling the intricate molecular networks that control disease.

Overview of Omics Technologies

Several omics technologies have emerged as new tools to probe specific cellular properties, namely genomics, epigenomics, transcriptomics, proteomics, lipidomics, metabolomics, multiomics, and integrated modalities (Fig. 1). A short description of the various omics methods can be found in Box 1, whereas a brief overview of the general workflow of each technology is shown in Figure 2. A nonexhaustive list and description of omics methods can also be found in Supplementary Table S1 and is meant to function as an initial reference for those unfamiliar with the variety of available omics technologies.

FIG. 1.

Overview of omics technologies and which elements of the cell they probe. Genomics and epigenomics analyze nucleic DNA; transcriptomics analyzes all RNA molecules (from messenger RNA in the nuclei to ribosomal RNA in the endoplasmic reticulum); proteomics and secretomics analyze proteins that are synthesized in the endoplasmic reticulum and transported to the membranes or lysosomes or secreted through the golgi apparatus and secretory vesicles; metabolomics analyzes metabolites produced and consumed by the cell and processed in the mitochondria; lipidomics analyzes lipid species that are located in membranes and organelles and play a role in metabolic processes. See Supplementary Table S1 for the definition of acronyms and a brief description of the omics technologies. Color images are available online.

FIG. 2.

Brief and general overview of the workflow and sample preparation of each omics technology from the 3D in vitro disease model to the data analysis. For omics analysis of EV molecular cargo, such as miRNA, lncRNA, lipids and proteins, an extra ultracentrifugation step (not shown in the figure) is performed to isolate the secreted EVs in the cell culture media before any specific extraction method to isolate cargo material for the appropriate omics strategy. 3D, three-dimensional; EV, extracellular vesicle; lncRNA, long-noncoding RNA; miRNA, microRNA; NMR, nuclear magnetic resonance. Color images are available online.

Current Landscape of Omics Technologies and 3D In Vitro Disease Models

Having provided an overview of some of the key omics technologies available, in this section we will highlight the types of studies to date that have combined omics technologies with 3D in vitro disease models. Due to the constraints of this review, we highlight only a few key examples in each case and Table 1 is not exhaustive.

Box 1.

Brief Descriptions of Different Omics Technologies, Namely Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, and Lipidomics

Genomics

The field of genomics involves the mapping of the nucleotide sequences of genomic DNA. Whole genome is distinguished from whole exome sequencing, which involves analysis of only the protein-coding regions of the genome. The development of massively parallel next-generation sequencing (NGS) has provided a high-throughput, rapid, and cheap technique for sequencing.⁵ One of the most significant milestones in genomics is the Human Genome Project, which fuelled the development of new sequencing techniques and analysis methods as well as provided the groundwork for mass spectrometry (MS)-based omics technologies.⁶

Epigenomics

Where genomics analyzes the nucleotide sequence of DNA, epigenomics focuses on the chromatin structures, histone modifications, and DNA methylation signatures of the genome that ultimately regulate whether genes are transcribed or silenced. These modifications do not alter the DNA sequence, but rather add chemical groups to the DNA molecule or histone proteins around which the DNA is coiled. This alters the structure of the chromatin and its accessibility to transcription factors.⁷ Different methods are available to analyze and sequence the epigenome. Some make use of the immunoprecipitation of chemical groups bound to DNA molecules (methylated DNA immunoprecipitation in combination with next-generation sequencing [meDIP-seq]) or histones (chromatin immunoprecipitation with massively parallel DNA sequencing [ChIP-seq]), others apply bisulfite treatment to reveal methylated sites (enhanced reduced representation bisulfite sequencing [eRRBS]) or use transposases to fragment accessible chromatin (Assay for Transposase-Accessible Chromatin with NGS [ATAC-seq]). Specifically, when combined with NGS to enable high-throughput sequencing, these epigenomics technologies have improved our understanding of epigenetic regulation of gene expression in the disease state.⁸

Transcriptomics

Transcriptomics is the study of a cell's transcriptome—the set of all RNA transcripts, including messenger RNA, transfer RNA, ribosomal RNA, long noncoding RNA, and microRNA.⁹ These transcripts contain information from genes for protein translation and can be quantified to show patterns of gene expression. Transcriptomics profile genes, which are transcribed into RNA, or the genes actively expressed by cells at a given point in time. This differs from Genomics, which instead profiles the cell's genomic DNA. Transcriptomics techniques include hybridization and sequencing approaches such as microarrays and RNA-sequencing (RNA-seq), respectively. Microarrays are relatively high-throughput and inexpensive but limited due to their requirement of predetermined knowledge of the genome sequence. In contrast, RNA-seq allows for an unbiased approach to map and quantify the transcriptome using NGS technologies originally developed for genomics studies.^10,11 More comprehensive methods, such as single-cell RNA sequencing (scRNA-seq),¹² single nuclei sequencing (scNuc-seq),¹³ and spatial transcriptomics¹⁴ also continue to gain popularity (Supplementary Table S1). This review will focus on sequencing-based transcriptomics due to rapid advances in NGS, resulting in the decline of microarray usage.¹⁵

Proteomics

Proteomics focuses on the large-scale study of proteins, which includes changes of the entire cellular proteome, post-translational modifications, and protein/protein interactions. Early proteomic methods included the well-established gel-run assays such as two-dimensional (2D) electrophoresis and western blots, however, these require a large amount of starting sample and have low-throughput efficacy.¹⁶ In response, the development of quantitative and high-throughput proteomic technologies with the ability to detect proteins at higher sensitivities have transformed the field, since unlike genomics and transcriptomics, proteomics cannot rely on polymerase chain reaction to increase quantity.¹⁷ These technologies can be broadly categorized into affinity-based assays, which often leverage antibodies to recognize proteins, including the multiplexed sandwich immunoassay through xMAP technology (Luminex)¹⁸ and protein microarrays,¹⁹ MS-based technologies,²⁰ and a combination of the two, including the more recent mass cytometry.²¹ Sub-branches of proteomics include phosphoproteomics and secretomics, which will be briefly discussed with a focus on proteins secreted from cellular assays.

Metabolomics

Metabolomics is the study of all products, or metabolites, generated as a result of ATP generation during cellular metabolism. As metabolism is a result of gene and protein expression in the context of the cell's environment, metabolomics is more reflective of cell phenotype than the omics discussed above. As the metabolic profile is immediately influenced by changes in cellular status or environment, rapid sample collection is key to capture a valid metabolic snapshot.²² As opposed to DNA, RNA, or proteins, metabolites are not composed of a consistent sequence of nucleotides or amino acids, but instead consist of a broad range of molecular structures and properties. Using techniques such as MS and nuclear magnetic resonance (NMR) for untargeted metabolomics, over 120,000 different metabolites have been identified in the human body.²³ Typically, only a subset of these metabolites are quantified in targeted metabolomic experiments as quantification demands the generation of metabolite-specific calibration curves.²⁴

Lipidomics

Often referred to as a branch of metabolomics, lipidomics is the study of all lipid species in the cell. While lipids play a role in ATP generation (through β-oxidation) and energy storage (high-energy bonds in fatty acid tails of glycerolipids), they are essential for many more cellular processes, justifying a separate lipidomics category. Lipids such as phospholipids are the foundation of cellular, nuclear, and organelle membranes, whereas cholesterol and ceramides are examples of important signaling molecules.²⁵ Due to their widespread functionality in cellular processes, they have an extensive range of molecular properties. Similar to metabolomics, MS or NMR are typically used to identify the structure and abundance of lipid species in lipidomics.²⁶

Multiomics and integrated modalities

Multiomics refers to performing two or more omics strategies separately in the same study. In contrast, integrated modalities are relatively new and exciting approaches developed specifically for the simultaneous analysis of cells using two or more omics strategies. An example is Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), which synchronously profiles both the transcriptome and proteome in single cells, providing information of the link between gene and protein expression in cells.²⁷

Table 1.

Omics Technologies Applied to Three-Dimensional In Vitro Disease Models

Study purpose/question driving omics study	Type of omics	Technique used	3D model	Disease modeled
Model validation with in vivo patient data	G	WGS	PDO	Cancer (gastrointestinal,^68,a pancreatic,^35,a,51,a breast,^52,a esophageal,^31,a colorectal,^37,125,a bladder^54,a)
		WES	PDO	Cancer (prostate,^47,a,55,a gastric,^28,a pancreatic,^35,a gynecologic tumor³⁰)
		WES	PDX/3D cellulosic sponge	Liver cancer^126,a
		Sc-WGS	PDO	Ovarian cancer^33,a
	E	ChIP-seq	PDO	Ulcerative colitis^43,a
	E	eRRBS	PDO	Prostate cancer^47,a
	T	RNA-seq	PDO	Cancer (gastric,^28,a,123,a gastrointestinal,^68,a prostate,^47,a,55,a pancreatic,^35,a,51,a breast,^52,a ovarian^33,a)
			PDO	Ulcerative colitis^43,a
			iPSC PDO	Colorectal cancer⁵⁰
			CRISPR-edited PDO	Cancer (brain,^53,56 bladder^54,a)
			Bacteria-infected PDO	Peptic ulcer⁴⁹
			PDX	Cancer (pancreatic,^51,a gastrointestinal^68,a)
			PDX/3D cellulosic sponge	Liver cancer^126,a
			Primary cells in microfluidic 3D coculture	Hepatic steatosis^127,a
		scRNA-seq	PDO	Pancreatic cancer^51,a
	P	CyToF	PDO	Breast cancer⁹⁰
		NanoLC-QOrbitrap MS	Multicellular cell line spheroid	Endometrial cancer⁹¹
		Membrane-based antibody arrays^b	Microfluidic model (LiverChip) and primary/cell line spheroid	Hepatitis B virus⁹³
	M	HPLC-QqQ/LIT MS	PD cells in 3D hydrogel system	Diabetic keratopathy¹⁰⁶
		LC-ESI-QOrbitrap MS	Cell line in 3D hydrogel system (TRACER)	Ovarian cancer¹¹²
		UHPLC-ESI-QOrbitrap MS	Primary cell spheroid	Hepatic steatosis^117,a
		UHPLC-ESI-QOrbitrap MS	Primary cell spheroid	Hepatic toxicity⁹⁹
		GC-EI-Q MS	Cell line with microbiome in microfluidic coculture system (HuMiX)	Disequilibrium of human/microbial crosstalk¹⁰⁵
		MALDI-QToF MSI and MALDI-FT-ICR MSI	Cell line spheroid	Bone cancer¹¹³
	L	UPLC-ESI-QToF MS	Primary cell spheroid	Hepatic steatosis^117,a
		DMS	Primary cells in microfluidic 3D coculture system	Hepatic steatosis^127,a
		HPLC-ESI-QOrbitrap MS	Mouse primary cell organoid	Acne vulgaris¹¹⁶
Model characterization and molecular profiling	G	WES	PDO	Cancer (gastric,^28,a colorectal,^29,a prostate^55,a)
			PDS and PDX	Brain cancer^128,a
			Primary cell spheroid	Colorectal cancer^34,a
		WGS	Microscaffold	Liver cancer^129,a
			PDO	Gastric cancer^123,a
			CRISPR-modified PDO and mouse organoid	Bladder cancer^54,a
	T	RNA-seq	PDO	Cancer (gastric,^28,a,123,a prostate,^55,apancreatic,^66,a colorectal^29,a)
			ESC/iPSC PDO	Bipolar disorder⁵⁸
				Schizophrenia⁵⁷
				Retinitis pigmentosa⁶³
			Bacteria-infected PDO	Cryptosporidium infection⁶¹
			CRISPR-modified PDO	Bladder cancer^54,a
				Bipolar disorder⁵⁹
				Autism spectrum disorder⁵⁹
				Schizophrenia⁵⁹
			Primary cell spheroid	Colorectal cancer^34,a
			Microscaffold	Liver cancer^129,a
			Microfluidic system (MDOTS/PDOTS)	Cancer (colorectal,⁶⁷ head and neck⁶⁷)
			PDS and PDX	Brain cancer^128,a
		scRNA-seq	PDO and PDX	Pancreatic cancer^51,a
			Multicellular PDO and murine organoid	Cancer (skin, kidney, lung^32,a)
			iPSC PDO	Blindness⁶²
	P	UPLC-nanoESI-QOrbitrap MS	Cell line spheroid	Colon cancer⁸⁶
		RPA	Cell line spheroid	Multiple cancers⁸⁷
		SDS-PAGE and UHPLC-LITQOrbitrap MS	iPSC PDO (OrgGloms)	Podocytopathies⁸⁵
		LC-QOrbitrap MS	3D hydrogel system	Cancer microenvironment (cancer-associated fibroblasts)⁸³
		HPLC-QToF MS	Cell line spheroid	Cancer of the nervous system⁸²
		NanoLC-ESI-QOrbitrap MS	Cell line in 3D-printed construct	Obesity, type II diabetes, metabolic disorders⁸⁴
		Antibody-coated beads (Milliplex MAP human cytokine/chemokine panel)^c	Multi cellular primary and cell line spheroid	Pancreatic cancer⁹⁴
		Multiplex bead-based ELISA^c	Microfluidic system (MDOTS/PDOTS)	Colon cancer⁶⁷
	M	¹³C-tracer-based IC-FT-ICR MS and ¹H NMR Spectroscopy	Magnetic 3D bioprinted (M3DB) cell line spheroid	Cancer (lung, pancreatic¹⁰²)
		¹H NMR Spectroscopy	Cell line spheroid	Breast cancer¹⁰¹
			Cell line spheroid	Skin cancer¹⁰⁰
			PDS and PDX	Brain cancer^128,a
		UHPLC-MS and GC-MS	Primary cell spheroid	Hepatic fibrosis⁹⁸
		UHPLC-ToF MS	Cell line spheroid	Cancer-associated herpesvirus infection¹⁰⁴
		Direct infusion ESI-QOrbitrap MS	Coculture of cell line on polymer scaffold with cell line in suspension	Leukemia¹⁰³
		FT-ICR-MALDI MSI	Cell line spheroid	Breast cancer¹¹⁴
	L	UPLC-ESI-QOrbitrap MS	Cell line spheroid	Breast cancer¹¹⁵
Understanding drug/therapy response	G	WGS	PDO	Cancer (ovarian,³⁶ esophageal^31,a)
	E	ATAC-seq	PDO	Colorectal cancer^44,a
	E	DNA methylation array	PDO	Colorectal cancer^37,a
	T	RNA-seq	PDO	Colorectal cancer^44,a,37,a
		RNA-seq	Microfluidic system (MDOTS/PDOTS)	Cancer (colorectal, head and neck⁶⁷)
		scRNA-seq	Multicellular PDO and murine organoid	Cancer (skin, kidney, lung^32,a)
	P	HPLC-ESI-QOrbitrap MS	3D primary cell coculture	Ovarian cancer^130,a
		2D-DIGE	Cell line spheroid	Bone cancer¹³¹
		HPLC-nanoESI-QOrbitrap MS	iPSC PDS	Alzheimer's disease⁸⁹
		MALDI-ToF-ToF MSI and UPLC-QOrbitrap MS	Cell line spheroid	Colorectal cancer⁸⁸
	M	GC-QToF MS	3D primary cell coculture	Ovarian cancer^130,a
	M	ESI-LITQ MSI	Cell line spheroid	Colorectal cancer^132,a
	L	ESI-LITQ MSI	Cell line spheroid	Colorectal cancer^132,a
		GC-MS	Cell line spheroid	Skin cancer¹¹⁹
		HPLC-LITOrbitrap MS and UPLC-QqQLIT MS	Cell line spheroid	Cancer (breast, prostate¹¹⁸)
Toxicity screen	E	meDIP-seq	Cardiac and hepatic primary spheroids (InSphero)	Toxicity of DMSO^46,a
	T	scRNA-seq	multicellular iPSC spheroid (Brainsphere)	Neurotoxicity^133,a
	T	RNA-seq and miRNA-seq	Cardiac and hepatic primary spheroids	Toxicity of DMSO^46,a
	P	nanoHPLC-QOrbitrap MS	Cardiac and hepatic primary spheroids (InSphero)	Toxicity of DMSO^46,a
	P	CE-ESI-ToF MS	3D coculture under air/liquid interface	Bronchial toxicity¹³⁴
	M	CE MS and HPLC-EI-QqQ MS	3D coculture under air/liquid interface	Bronchial toxicity¹³⁴
	M	LC-ESI-QToF MS	multicellular iPSC spheroid (Brainsphere)	Neurotoxicity^133,a
Patient stratification	G	Sc-WGS	PDO	Ovarian cancer^33,a
	T	RNA-seq	PDO	Cancer (gastrointestinal,^68,a pancreatic,^35,a ovarian^33,a)
	T	RNA-seq	PDX	Gastrointestinal cancer^68,a
	P	UHPLC-Orbitrap MS and UHPLC-QOrbitrap MS	PDO	Colorectal cancer⁹²
Disease mechanism discovery (disease initiation, development, and progression)	G	WES	Primary organoid	Normal tissue³⁸
	E	ChIP-seq	PDO	Prostate cancer⁴¹
			Mouse primary organoid	Prostate cancer⁴²
			3D hydrogel coculture of human or mouse cells	Pancreatic cancer^45,a
		ChIP-seq and ATAC-seq	Transgenic primary mouse organoid	Pancreatic cancer^48,a
	T	RNA-seq	PDO	Cancer (prostate,^55,a pancreatic^66,a)
			iPSC/ESC PDO	Schizophrenia⁵⁷
			iPSC/ESC PDO	Retinitis pigmentosa⁶³
			Bacteria-infected PDO	Cryptosporidium infection⁶¹
			Cardiac and hepatic primary spheroids (InSphero)	Drug-induced cardiotoxicity⁶⁴
			3D primary coculture (MucilAir)	Smoking-induced nasal inflammation¹³⁵
			Vascularized multicellular 3D scaffold (BioVasc)	Gastroenteritis⁶⁵
			ESC-derived virus-infected organoid	Zika virus infection⁶⁰
			Murine organoid	Paneth cell dysfunction^74,a
			Transgenic primary mouse organoid	Pancreatic cancer^48,a
			3D hydrogel coculture of human or mouse cells	Pancreatic cancer^45,a
		scRNA-seq	iPSC PDO	Blindness⁶²
	P	nanoLC-nanoESI-LITOrbitrap MS	PDO	Pancreatic cancer^66,a
		nanoLC-ESI-QOrbitrap MS	3D organ-on-a-chip (Biowire)	Myocardial fibrosis⁷⁵
		nanoLC-ESI-QOrbitrap MS	Murine organoids	Paneth cell dysfunction^74,a
		UHPLC-QOrbitrap MS	Primary cell organoid coculture	Atopic eczema^136,a
		LC-ESI-QLITOrbitrap MS	Cell line embedded in 3D collagen gel	Cancer invasion⁷⁶
	M	UHPLC-ESI-ToF MS	Cell line in 3D microfluidic system	Gut metabolite-sensing by kidney¹⁰⁹
		UHPLC-ToF MS	Cell line spheroid	Cancer-associated herpesvirus infection¹⁰⁴
		CE-ESI-ToF MS and CE-ESI-QqQ MS	Cell line in 3D suspension culture	Breast cancer¹⁰⁷
		GC-EI-ToF MS	Cell line spheroid	Ovarian cancer¹⁰⁸
		¹H NMR Spectroscopy	Cell line spheroid	Skin cancer¹⁰⁰
		GC-EI-Q MS	PDO	Pancreatic cancer¹³⁷
		HPLC-ESI-QOrbitrap MS	PDX	Colorectal cancer¹³⁸
		HPLC-QqQLIT MS	3D hydrogel coculture of human or mouse cells	Pancreatic cancer^45,a
		¹³C-tracer-based GC-CI-Q MS	Cell line spheroid	Prostate cancer¹¹¹
	L	UHPLC-ESI-QOrbitrap MS	Cell line spheroid	Breast cancer¹¹⁵
		UHPLC-Orbitrap MS	Primary cell organoid coculture	Atopic eczema^136,a
		HPLC-ESI-QToF MS and GC-EI-Q MS	Cell line spheroid	Breast cancer¹³⁹
Drug target discovery	G	CRISPR/Cas9 screening integrated with NGS	PDO and PDX	Colorectal cancer³⁹
	E	ChIP-seq	PDO	Ulcerative colitis^43,a
	T	RNA-seq	PDO	Gastric cancer^123,a
	T	RNA-seq	PDO	Ulcerative colitis^43,a
	M	CE-ESI-ToF MS and CE-ESI-QqQ MS	Cell line spheroid	Cancer (ovarian, cervical⁹⁷)
Clonal selection	G	WES	PDO	Colorectal cancer^37,a
Clonal selection	T	RNA-seq	PDO	Colorectal cancer^37,a

Overview of studies that have used omics technologies to address questions related to 3D in vitro disease models. The omics technologies in this table are described in as much detail as the specific study provided. The definition of the acronyms of the omics technologies and a brief description can be found in Supplementary Table S1.

Multiomics study.

Study applied proteomics and secretomics.

Study applied secretomics.

2D, two-dimensional; 3D, three-dimensional; ATAC-seq, Assay for Transposase-Accessible Chromatin with NGS; ChIP-seq, chromatin immunoprecipitation with massively parallel DNA sequencing; E, epigenomics; ELISA, enzyme-linked immunosorbent assay; eRRBS, enhanced reduced representation bisulfite sequencing; ESC, embryonic stem cell; G, genomics; iPSC, induced pluripotent stem cell; L, lipidomics; M, metabolomics; MDOTS, murine-derived organotypic tumor spheroid; meDIP-seq, methylated DNA immunoprecipitation in combination with next-generation sequencing; miRNA, microRNA; MS, mass spectrometry; MSI, mass spectrometry imaging; NGS, next-generation sequencing; NMR, nuclear magnetic resonance; P, proteomics; PD, patient-derived; PDO, patient-derived organoid; PDOTS, patient-derived organotypic tumor spheroid; PDS, patient-derived spheroid; PDX, patient-derived xenograft; RNA-seq, RNA-sequencing; scRNA-seq, single-cell RNA sequencing; T, transcriptomics; WES, whole exome sequencing; WGS, whole genome sequencing; CyToF, mass cytometry by time of flight; DIGE, difference gel electrophoresis; miRNA-seq, microRNA sequencing; RPA, reverse phase protein microarray; sc-WGS, single-cell whole genome sequencing; SDS-PAGE, sodium dodecyl sulphate-polyacrylamide gel electrophoresis.

Genomics technologies for 3D in vitro disease

Genomics technologies, such as next-generation sequencing (NGS), have been applied to the complete characterization of the genotype of 3D disease models to classify disease subtypes. For example, model characterization by means of genomics, specifically NGS, has contributed to the creation of “living biobanks” of PDOs and patient-derived xenografts.^28,29 These biobanks have greatly improved the availability of such models, and have enabled many groups to investigate disease phenomena associated with specific patient subtypes in these 3D in vitro systems.

Building on model characterization, another key application of genomics in the context of 3D disease models has been model validation by comparing the disease model to patient tissue data, to determine to what extent the model resembles the in vivo disease genotype. For example, whole exome sequencing (WES) and whole genome sequencing (WGS) were used to investigate how the mutational landscape of 3D tumor models recapitulates that of patient tumor samples.^28–32 WES and WGS were further used to demonstrate that organoids capture the intratumoral heterogeneity, an important feature of in vivo tumors.^28,33,34

With validated models now emerging, a combination of genomics and tissue models has now begun to contribute to the identification of gene signatures that are predictive of response to therapy for personalized medicine applications.³⁵ For example, studies using WGS³⁶ and WES³¹ have facilitated the understanding of how drug sensitivity is related to genomic mutation profile in PDOs. Interestingly, these correlations were not detectable in monolayer cultures,³⁶ highlighting the relevance of 3D disease models. Furthermore, another emerging application for genomics studies of 3D disease models is the investigation of disease mechanisms and progression. For example, WGS has facilitated the probing of clonal evolution in PDOs to discover cancer drivers,³⁷ while WES was used to map the accumulation pattern of mutations in patient-derived adult stem cell organoids to investigate tumorogenesis.³⁸ Additionally, whole-genome perturbations in clonal organoids, executed by CRISPR/Cas9 and screened with NGS, have enabled the precise identification of mutations that confer therapy resistance.^39,40 As more validated models emerge, such approaches will be useful to stratify patients for the purpose of developing precision medicine applications.

Epigenomics technologies for 3D in vitro disease models

Epigenomics technologies are valuable tools for probing 3D disease models due to the discovery of distinct epigenetic signatures that associate with specific disease phenotypes. In the context of 3D disease models, epigenomics has therefore most frequently been used to address disease mechanisms associated with specific phenotypes and therapy resistance. For example, the coupling of chromatin immunoprecipitation with massively parallel DNA sequencing (ChIP-seq) identified the DNA-binding sites of transcription factors critical to prostate^41,42 and colon⁴³ oncogenesis in organoid models. Additionally, the role of chromatin accessibility in the chemoresistance colon cancer organoids was investigated using the Assay for Transposase-Accessible Chromatin with NGS (ATAC-seq).⁴⁴ Similarly, epigenomics has facilitated the investigation of the alterations of epigenetic signatures upon stimulation with environmental factors that impact phenotypes. For example, ChIP-seq demonstrated the effect of stromal cues on histone acetylation in a 3D pancreatic cancer model.⁴⁵ Furthermore, methylated DNA immunoprecipitation in combination with next-generation sequencing (meDIP-seq) was employed to investigate the DNA methylation profile of cardiac and hepatic spheroids in response to stimulation with a toxic agent.⁴⁶ In addition, as with genomic analysis, one of the earliest applications of epigenomics has been to validate novel 3D disease models by comparing to patient samples. For example, ChIP-seq has been employed to investigate if organoids recapitulate epigenetic characteristics of patient tissue.⁴³ Additionally, enhanced reduced representation bisulfite sequencing (eRRBS) has demonstrated correlation of prostate cancer organoid DNA methylation to their corresponding patient tissue samples.⁴⁷

Increasingly emerging are multiepigenomics approaches that provide more insight into the various types of epigenetic signatures and their association to disease. For example, the combination of ChIP-seq and ATAC-seq has allowed the analysis of DNA/protein-binding sites as well as open chromatin sites of 3D tumor models, correlating their broad epigenetic profile to metastatic transition.⁴⁸ Such integrative approaches to epigenomics have the potential to elucidate the full regulatory mechanism that determines gene expression and eventually cell phenotype.

Transcriptomics technologies for 3D in vitro disease models

Transcriptomics is perhaps the most widely used omics technology with 3D in vitro disease models and has been applied to answer a variety of questions. As with genomic analysis, transcriptomics studies have mainly been driven by bulk RNA sequencing (RNA-seq) for the purpose of validating models by comparing them to patient tissue, as shown in Table 1, further highlighting the importance of this initial validation step to confirm the relevance of 3D disease models for biological discovery. Often, these studies show the ability of 3D models such as PDOs to recapitulate the patient transcriptional landscape at various disease stages,⁴⁹ as well as match patient response to therapies.^{28,29,47,50–55} Several model validation studies on PDOs have also aided the assessment of PDO transplantability in vivo⁵⁶ and the generation of living biobanks or centralized PDO distribution centers.^28,29,52,54 These characterization efforts have also enabled the profiling of cell population heterogeneity of 3D disease models, in combination with validation and mechanism-discovery studies. Such profiling has led to the discovery of novel disease features in otherwise inaccessible tissues like the brain, using induced pluripotent stem cell-derived organoids,^57–59 and to the identification of spatial³⁴ and temporal transcriptional changes,^60–62 using PDOs. These applications would be challenging to decipher in patient samples.

Beyond model validation, one of the most powerful applications that combining transcriptomics and 3D disease models is enabling are studies driven by biological questions that are difficult to address with clinical samples. Such studies are focused on understanding the mechanisms of disease development or progression,^43,63–66 and drug response.^37,67 For example, the groups of Valeri⁶⁸ and Tuveson³⁵ treated PDOs from different patients with various chemotherapeutics and performed pharmacotranscriptomics to stratify and explain transcriptional variations to therapy response. Similarly, Romero-Calvo et al. showed that this varied therapy response was due to patient-specific clonal diversity.⁵¹ These works especially highlight the value of 3D disease models and transcriptomics for precision medicine.

An emerging trend in transcriptomics and 3D disease models is the use of single-cell approaches. Single-cell techniques have been widely adopted in the investigation of patient samples and in vivo systems and can also address heterogeneity of 3D disease models. For example, heterogeneity associated with different cell types or heterogeneity within one cell type, for example, cancer cells, stem cells, and immune cells. However, this approach has not yet been used extensively in the context of 3D disease models, perhaps because there is a lack of existing multicellular 3D disease models requiring single-cell resolution. One of the few studies integrating single-cell techniques includes Neal et al.'s single-cell RNA sequencing (scRNA-seq) study on multicellular PDOs with autologous immune infiltrates, which showed that mechanisms of failed immunotherapy were due to expansion of exhausted T cells in particular.³² This work emphasizes the potential of integrating single-cell approaches with physiologically relevant multicellular 3D disease models for exploring specific hypothesis-driven questions, such as deciphering cell-specific contributions to therapy resistance.

Lastly, another emerging trend is transcriptomic profiling of extracellular vesicle cargo, such as microRNA (miRNA) and long noncoding RNA (lncRNA), in 3D disease models due to their roles in disease progression,^69–71 diagnostics,⁷² and therapeutics.⁷³ For example, lncRNA has been shown to impact tumorigenicity in breast cancer organoids,⁷¹ whereas miRNA has demonstrated potential as a novel therapeutic target in ovarian cancer organoids.⁷³ These transcriptomic studies performed in 3D disease models are especially difficult to do in vivo and have been mainly limited to microarray analysis (for miRNA) and bulk RNAseq (for lncRNA).

Proteomics technologies for 3D in vitro disease models

Compared with transcriptomics, the use of proteomics to investigate 3D disease models is less extensive. This can be partially attributed to the still growing and evolving field of proteomics technologies that, despite having made significant advancements, has not established standard and widely available protocols for use with 3D disease models. Nevertheless, understanding disease development and progression using proteomic profiles has provided insight in areas that cannot be assessed using genomics and transcriptomics in 3D disease models.^66,74–76 Meanwhile, proteomic profiling has been more frequently used with established models, such as the two-dimensional (2D) in vitro models, in vivo murine models, and patient tissue and serum samples, and has contributed to major findings that have had clear clinical applications.^77–81

In the context of 3D disease models, proteomic profiling has mainly been applied to address questions that compare 2D and 3D culture systems. Several studies have demonstrated a clear distinction between the protein profiles of 2D and 3D cultures, with the latter retaining more similarities to in vivo disease states.^82–85 Similarly, the phosphoproteome, proteins that contain a post-translational phosphate group modification that guides many cellular processes such as DNA replication, apoptosis, and invasion, has also demonstrated distinct differences between 2D and 3D cultures.^86,87 Moreover, proteomics has elucidated differences in drug mechanism and chemotherapeutic resistance between 2D and 3D culture model studies such as drug penetration, dose-dependent drug efficiency, and drug clearance of colorectal cancer⁸⁸ and Alzheimer's disease models.⁸⁹

A few studies have used proteomics as the foundation to compare 3D disease model data with in vivo patient data. These studies used PDOs and spheroids to demonstrate the preservation of in vivo protein expression patterns.^90,91 Similarly, stratifying patient populations using 3D disease models and proteomics is not common, likely due to the lack of available proteomic patient databases. Moreover, the inherent dynamic characteristic of proteins in disease adds complexity. A single recent study is, to our knowledge, the only example of proteomic profiling of human organoids using mass spectrometry that was correlated with clinical diagnosis and importantly, addressed the interpatient heterogeneity.⁹²

Secretome analysis of 3D disease models has relied on immunoassay-based technologies. These studies have largely focused on the human immune system and its defining network of secreted proteins, including cytokines, chemokines, and growth factors. For example, membrane-based immunoassay arrays were used to demonstrate that the innate immune and cytokine responses in a 3D disease model recapitulated those observed in Hepatitis B virus-infected patients.⁹³ Additionally, bead-based multiplexed immunoassays were used to characterize the levels of cytokines and growth factors in the supernatant of multicellular spheroid tricultures,⁹⁴ and the cytokine profile of a novel 3D culture system to model immune checkpoint blockade.⁶⁷

There is huge untapped potential in enhancing disease discoveries and knowledge through the combination of 3D disease models and proteomics. There are powerful proteomics technologies, as listed in Figure 1 that have seldom or never been leveraged for 3D disease models, such as single-cell proteomics using mass cytometry⁹⁵ and spatial proteomics technologies.⁹⁶ The proteome plays a significant role in disease development and progression, as it is the main functional machinery of cells and the source of communication between cells, thereby linking heterogeneous disease cell populations. As such, there are key questions that would benefit from proteomics analysis of 3D disease models that have not yet been addressed. The application of proteomics to analyze 3D disease models is therefore an important evolving area for the future.

Metabolomics technologies for 3D in vitro disease models

Metabolomics provides a more comprehensive characterization of cellular phenotype than the omics technologies discussed above. One of the most thoroughly explored applications of metabolomics in the context of 3D disease models is the demonstration of improved relevance of 3D disease models compared with traditional 2D monolayers. For example, mass spectrometry and nuclear magnetic resonance-based metabolomics have revealed that 3D tumor models demonstrate a significant difference in metabolic profile,^97–100 as well as an increase in therapy resistance^101–103 in comparison to 2D monolayers. Additionally, metabolomics of 3D disease models revealed new disease pathways that were not recapitulated in traditional monolayers.¹⁰⁴ These studies have successfully demonstrated the use of metabolomics to highlight the importance of physiologically relevant 3D disease models for drug screening and drug target discovery.

Metabolomics has also been applied as a tool to validate if the 3D disease model appropriately recapitulates the in vivo phenotype.¹⁰⁵ For example, a novel 3D human/microbiome microfluidics system (HuMiX) was validated relative to datasets from clinical studies.¹⁰⁵ Additionally, analogous to the field of precision medicine, metabolomics has assisted in determining if a 3D model maintains disease subtype,¹⁰⁶ as well as donor-specific metabolic characteristics.⁹⁹

Another interesting use of metabolomics analysis in the context of 3D disease models is the investigation of disease mechanisms as well as physiological interactions. For example, metabolomics studies investigated the metabolic reprogramming of cancer cells and correlated this to their proliferative¹⁰⁷ or metastatic¹⁰⁰ phenotype in a spheroid model. Other groups have found significant differences in metabolic processes between cancer cells and their (isogenic) cancer stem cell counterpart in spheroids.^97,108 Metabolomics has also been applied to the investigation of physiological systems, such as that of the symbiotic human/microbiome axis. For example, Jansen et al. investigated the sensing and signaling of gut/microbiome metabolites in a 3D kidney tissue model.¹⁰⁹ As an extension of metabolomic profiling, an isotope-resolved branch of metabolomics that traces an isotope-labeled metabolite (fluxomics) has assisted in the investigation of specific metabolomic pathways and has identified intracellular metabolic fluxes in 3D disease models.¹¹⁰ For example, several groups have used fluxomics to investigate metabolic reprogramming¹¹¹ or drug response¹⁰² of cancer spheroids.

A limitation of the described studies is that bulk metabolite collection does not allow for spatially resolved metabolomics and does not capture the heterogeneity within the 3D disease model. To address this, our group recently established a 3D culture system that can be rapidly disassembled for the efficient collection of the unstable metabolites to investigate metabolic profiles within graded microenvironments. Using our TRACER system, we employed mass spectrometry to correlate cell consumption-driven gradients of hypoxia to metabolic signatures.¹¹² To our knowledge, TRACER is the only model that offers rapid sample collection for the purpose of performing spatially resolved metabolomics. Other approaches to spatially resolve metabolomics in 3D disease models include mass spectrometry imaging (MSI), which has been used to spatially map metabolic signatures to investigate response to chemotherapy¹¹³ and to indicate regions of hypoxia and oxidative stress¹¹⁴ in multicellular spheroids. The collection of spatial information, either through designing inventive 3D disease model architecture or by applying MSI technology, will further exploit the use of metabolomics in 3D disease models.

Lipidomics technologies for 3D in vitro disease models

Lipidomics studies are used to investigate lipid species that are involved in metabolomic processes, associated to signaling pathways or integral to nuclear and cellular membrane structures. Although not extensively utilized in the context of 3D disease models, lipidomics has demonstrated potential to address various questions relevant to 3D systems. Similar to proteomics, lipidomics has been used as a strategy to compare 3D disease models to traditional monolayers as well as to patient samples. For example, mass spectrometry was employed to demonstrate significant differences between 3D disease models and 2D monolayers,¹¹⁵ while it has identified similar lipid profiles in 3D disease models compared with those observed in diseased organ in vivo.^116,117 Several lipidomics studies in the context of relevant 3D disease models have also addressed questions regarding disease progression, drug response, and therapy resistance. For example, lipidomics has been used to investigate the disease mechanism and progression of conditions directly related to a dysfunctional lipid homeostasis such as hepatic steatosis¹¹⁷ or acne vulgaris.¹¹⁶ Lipidomics has also shown to be a valuable tool to investigate the effect drug treatment on lipid metabolism,¹¹⁸ and the effect of altered lipid metabolism on therapy resistance in physiologically relevant 3D tumor models.¹¹⁹

Emerging in the field of lipidomics are spatially resolved approaches that provide information on cellular lipid function. For example, lipidomics with mass spectrometry in combination with confocal Raman microscopy has spatially mapped lipid species within the 3D disease model and correlated these lipid profiles to cell malignancy.¹¹⁵ To further improve sensitivity and resolution, another study combined confocal Raman microscopy with matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging in which a full mass spectrum is generated from each MALDI laser position to resolve metabolite and lipid species.¹²⁰ These developments in spatially resolved lipidomics in 3D disease models are of great value to improve our understanding of the broad range of lipid functionalities in the context of disease.

Multiomics technologies and integrated modalities for 3D in vitro disease models

Multiomics involves extracting a variety of information by performing multiple omics technologies in a single study, and has been shown to provide complementary information in validating, characterizing and discovering new biology with 3D disease models.^121,122 In the most developed tissue models, such as cancer organoids, transcriptomics and genomics have been performed together to validate the genetic mutations and transcriptional landscape of the 3D disease model with the patient tissue.^{28,32,35,52,54,55,123} Other combinations shown in Table 1 include transcriptomics and epigenomics to link epigenetic regulators and disease progression,^43,47 transcriptomics and proteomics^66,74 to understand disease mechanisms, and even a combination of all omics types to perform a toxicity screen.⁴⁶ In contrast to multiomics, integrated modalities simultaneously perform two or more omics technologies, such as transcriptomics and proteomics (Cellular Indexing of Transcriptomes and Epitopes by sequencing [CITE-seq])²⁷ or transcriptomics and epigenomics (single-cell Chromatin Overall Omic-scale Landscape sequencing [scCOOL-seq]).¹²⁴ However, integrated modalities have not been used yet to investigate 3D in vitro disease models despite its high potential for improving our understanding of disease.

Experimental Design Considerations and Challenges of Omics Technologies and 3D In Vitro Models

Factors to consider when designing omics experiments to obtain meaningful data

There are additional factors and challenges that need to be considered when using omics assays with 3D disease models, on top of those typically accounted for in 2D in vitro systems. This will be broadly discussed here across omics disciplines. The overarching challenge unique to 3D disease models is the isolation of cellular components required for the respective omics technology and corresponding biological question, to obtain a representative dataset. Across 3D disease models mentioned in Table 1, the first step is to remove cells from the 3D disease model, through dissociation and digestion protocols. Spheroids and organoids are regularly harvested, digested, and RNA or DNA molecules are extracted using kit reagents. Other common reagents include TRIzol, TrypLE (typically for Matrigel), and a range of enzymes such as trypsin, proteases, and collagenases.^32,51 Selecting the optimal dissociation and digestion protocol relies on balancing a high cell yield and cell viability. For further in-depth discussion on such protocols, readers are referred to Worthington Biochemical Corporation's Cell Isolation Optimizing System manual.¹⁴⁰ For more complex and “novel” 3D systems, specific steps must be performed. This may involve mechanical dissociation by means of tearing the nonbiological component of a 3D cellulosic sponge system to release cells,¹²⁶ detaching cells from membranes of a microfluidic system using a plastic scrapper,¹⁰⁵ and pipetting through a modified pipet tip.⁴⁵

Components in 3D tissue models, such as nonbiological scaffolds, extracellular matrix (ECM), cell-specific properties, and the rarity of cell populations can also complicate obtaining a complete starting sample. For example, cell adhesion to substrates used to construct the tissue, such as paper scaffolds and cellular sponges, will complicate the digestion protocol. Certain phenomena such as fibrosis,⁷⁵ migration, and invasion⁷⁶ have been shown to require an ECM to sufficiently mimic in vivo states. As such, the ECM may present an additional physical barrier to properly access cells. Moreover, the cells used in the 3D disease model can be derived from robust cell lines as well as from primary patient samples, which presents another parameter to consider. Although the availability of primary patient samples has been greatly improved by the implementation of living biobanks, these populations are not extensive and often do not include patient-matched samples of cell types essential to disease progression, such as supporting cells in the tumor microenvironment.¹⁴¹ In these cases, cells that have not previously been expanded will likely be limited in supply, thereby decreasing initial sample quantity. In addition, for analyses of individual cell types within multiculture systems, an extra step to sort cells through fluorescence-activated cell sorting is required. However, this is conducive to losing fragile and sparse samples, and potentially results in the enrichment of more robust cells. The take-home message is that the optimal digestion and dissociation protocol making use of specific enzymes, digestion times, and shaking speeds, will differ across systems, and often need to be systemically designed and optimized for each specific model.

Barriers and challenges associated with omics

Tissue engineers experience several barriers that come with integrating omics technologies with 3D in vitro models. First, limited access to equipment and facilities can be an obstacle. Not all institutions are equipped with omics-related facilities and seeking these out through interinstitution or international contacts can be logistically difficult. Second, the omics experiment, analysis, and facility fees can be costly, especially for novel techniques that have not been widely adopted yet. For this reason, it is important for the researcher to fully understand the purpose of using the omics approach and the significance of the question that is being addressed. Thirdly, the difficulty to obtain bioinformatics expertise for analyzing the large amounts of raw data provides a barrier to entry. Training is usually required to obtain useful information from complicated omics datasets and the selection of appropriate software and tools is not standardized.¹²¹ To get started with analysis of omics datasets, we recommend looking into popular tools that comprehensively describe approaches for data analysis and have been extensively reviewed by others (Supplementary Table S2). However, as omics technologies gain popularity and, arguably, necessity in discovering new biology, there will be a strong demand for institutions to centralize omics facilities and for leaders in the field to standardize omics analysis protocols. With this support, the associated barriers may dissolve and render omics technologies significantly more accessible for tissue engineers.

In addition to these logistical barriers to implement omics technologies, teams developing 3D disease models must prioritize functionally validating the biological relevance of their models. This provides confidence that hypotheses or mechanisms derived from omics analyses are relevant to the in vivo phenomenon being modeled. Such model validation is nontrivial as this ideally requires the assay to provide a functional readout of the 3D disease model that can be directly mapped to an in vivo tissue functional property, such as drug resistance or toxicity. Datasets that enable this functional validation are available for only a few disease models such as cardiac toxicity,¹⁴² cancer,^35,68 and cystic fibrosis.¹⁴³ Omics technologies have provided mechanisms to assess the similarity of the disease model to patient samples. However, without a functional assay, the importance of any observed gene differences, and whether they render the model predictive or not, is challenging to assess. Partnering with clinicians to link relevant clinical datasets and readouts to validate model predictivity in disease models is therefore, an important goal for the future.

Conclusion: Perspective on Omics Technologies for Future Applications in 3D In Vitro Models

The combination of tissue models and omics technologies is emerging as a powerful tool to benchmark models to clinical samples, identify new targets and understand disease mechanisms. As adoption of complex, spatial, and multiomics approaches is still limited and the field is early in development, there are a number of technical and logistical barriers that make integrating these technologies difficult. Currently, there are also challenges associated with functional validation of the model before assessing with omics technologies. However, the potential of combining 3D in vitro disease models with omics to understand disease more deeply is extremely promising and will provide the opportunity to better link genotype to phenotype in multicellular complex systems.

Footnotes

Acknowledgments

The authors would like to acknowledge Michail Paraskevopoulos for contributions to the literature search, and Ziting (Judy) Xia and Margaret Ho for contributions to resources included in this article.

Disclosure Statement

The authors have no conflicts of interests to declare.

Funding Information

This work was funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant to A.P.M. (Fund #RGPIN-2019-06486) and an NSERC CREATE Fellowship to NW.

Supplementary Material

References

Karczewski

K.J.

, and Snyder

M.P.

Integrative omics for health and disease. Nat Rev Genet, 19, 299, 2018.

Olivier

, Asmis

, Hawkins

G.A.

, et al. The need for multi-omics biomarker signatures in precision medicine. Int J Mol Sci, 20, 4781, 2019.

McShane

L.M.

, Cavenagh

M.M.

, Lively

T.G.

, et al. Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration. BMC Med, 11, 220, 2013.

Ibrahim

, Pasic

, and Yousef

G.M.

Omics for personalized medicine: defining the current we swim in. Expert Rev Mol Diagn, 16, 719, 2016.

Heather

J.M.

, and Chain

The sequence of sequencers: the history of sequencing DNA. Genomics, 107, 1, 2016.

Hood

, and Rowen

The human genome project: big science transforms biology and medicine. Genome Med, 5, 79, 2013.

Klemm

S.L.

, Shipony

, and Greenleaf

W.J.

Chromatin accessibility and the regulatory epigenome. Nat Rev Genet, 20, 207, 2019.

Wang

K.C.

, and Chang

H.Y.

Epigenomics technologies and applications. Circ Res, 122, 1191, 2018.

Wang

, Gerstein

, and Snyder

RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet, 10, 57, 2009.

10.

Nagalakshmi

, Wang

, Waern

, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science (80-), 320, 1344, 2008.

11.

Wilhelm

B.T.

, Marguerat

, Watt

, et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature, 453, 1239, 2008.

12.

Tang

, Barbacioru

, Wang

, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods, 6, 377, 2009.

13.

Grindberg

R.V.

, Yee-Greenbaum

J.L.

, McConnell

M.J.

, et al. RNA-sequencing from single nuclei. Proc Natl Acad Sci U S A, 110, 19802, 2013.

14.

Ståhl

P.L.

, Salmén

, Vickovic

, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science (80-), 353, 78, 2016.

15.

Lowe

, Shirley

, Bleackley

, et al. Transcriptomics technologies. PLoS Comput Biol, 13, e1005457, 2017.

16.

Hanash

Disease proteomics. Nature, 422, 226, 2003.

17.

Cox

, and Mann

Is Proteomics the New Genomics? Cell, 130, 395, 2007.

18.

Dunbar

S.A.

Applications of Luminex^® xMAP™ technology for rapid, high-throughput multiplexed nucleic acid detection. Clin Chim Acta, 363, 71, 2006.

19.

Melton

Proteomics in multiplex. Nature, 429, 101, 2004.

20.

Aebersold

, and Mann

Mass spectrometry-based proteomics. Nature, 422, 198, 2003.

21.

Bandura

D.R.

, Baranov

V.I.

, Ornatsky

O.I.

, et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem, 81, 6813, 2009.

22.

Smith

, Villaret-Cazadamont

, Claus

S.P.

, et al. Important considerations for sample collection in metabolomics studies with a special focus on applications to liver functions. Metabolites, 10, 104, 2020.

23.

Wishart

D.S.

, Feunang

Y.D.

, Marcu

, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res, 46, D608, 2018.

24.

Broadhurst

, Goodacre

, Reinke

S.N.

, et al. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics, 14, 72, 2018.

25.

Eyster

K.M.

The membrane and lipids as integral participants in signal transduction: lipid signal transduction for the non-lipid biochemist. Adv Physiol Educ, 31, 5, 2007.

26.

Jurowski

, Kochan

, Walczak

, et al. Analytical techniques in lipidomics: state of the art. Crit Rev Anal Chem, 47, 418, 2017.

27.

Stoeckius

, Hafemeister

, Stephenson

, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods, 14, 865, 2017.

28.

Yan

H.H.N.

, Siu

H.C.

, Law

, et al. A Comprehensive Human Gastric Cancer Organoid Biobank Captures Tumor Subtype Heterogeneity and Enables Therapeutic Screening. Cell Stem Cell, 23, 882, 2018.

29.

Van De Wetering

, Francies

H.E.

, Francis

J.M.

, et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell, 161, 933, 2015.

30.

Maru

, Tanaka

, Itami

, et al. Efficient use of patient-derived organoids as a preclinical model for gynecologic tumors. Gynecol Oncol, 154, 189, 2019.

31.

, Francies

H.E.

, Secrier

, et al. Organoid cultures recapitulate esophageal adenocarcinoma heterogeneity providing a model for clonality studies and precision therapeutics. Nat Commun, 9, 1, 2018.

32.

Neal

J.T.

, Li

, Zhu

, et al. Organoid modeling of the tumor immune microenvironment. Cell, 175, 1972, 2018.

33.

Kopper

, de Witte

C.J.

, Lõhmussaar

, et al. An organoid platform for ovarian cancer captures intra- and interpatient heterogeneity. Nat Med, 25, 838, 2019.

34.

Árnadóttir

S.S.

, Jeppesen

, Lamy

, et al. Characterization of genetic intratumor heterogeneity in colorectal cancer and matching patient-derived spheroid cultures. Mol Oncol, 12, 132, 2018.

35.

Tiriac

, Belleau

, Engle

D.D.

, et al. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer. Cancer Discov, 8, 1112, 2018.

36.

Jabs

, Zickgraf

F.M.

, Park

, et al. Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations. Mol Syst Biol, 13, 955, 2017.

37.

Roerink

S.F.

, Sasaki

, Lee-Six

, et al. Intra-tumour diversification in colorectal cancer at the single-cell level. Nature, 556, 437, 2018.

38.

Blokzijl

, De Ligt

, Jager

, et al. Tissue-specific mutation accumulation in human adult stem cells during life. Nature, 538, 260, 2016.

39.

Michels

B.E.

, Mosa

M.H.

, Streibl

B.I.

, et al. Pooled in vitro and in vivo CRISPR-Cas9 screening identifies tumor suppressors in human colon organoids. Cell Stem Cell, 26, 782, 2020.

40.

Ringel

, Frey

, Ringnalda

, et al. Genome-scale CRISPR screening in human intestinal organoids identifies drivers of TGF-β resistance. Cell Stem Cell, 26, 431, 2020.

41.

Berger

, Brady

N.J.

, Bareja

, et al. N-Myc-mediated epigenetic reprogramming drives lineage plasticity in advanced prostate cancer. J Clin Invest, 129, 3924, 2019.

42.

Bose

, Karthaus

W.R.

, Armenia

, et al. ERF mutations reveal a balance of ETS factors controlling prostate oncogenesis. Nature, 546, 671, 2017.

43.

Sarvestani

S.K.

, Signs

S.A.

, Lefebvre

, et al. Cancer-predicting transcriptomic and epigenetic signatures revealed for ulcerative colitis in patient-derived epithelial organoids. Oncotarget, 9, 28717, 2018.

44.

Tung

K.L.

, Chen

K.Y.

, Negrete

, et al. Integrated chromatin and transcriptomic profiling of patient-derived colon cancer organoids identifies personalized drug targets to overcome oxaliplatin resistance. Genes Dis 2019 [Epub ahead of print]; DOI: https://doi.org/10.1016/j.gendis.2019.10.012.

45.

Sherman

M.H.

, Yu

R.T.

, Tseng

T.W.

, et al. Stromal cues regulate the pancreatic cancer epigenome and metabolome. Proc Natl Acad Sci U S A, 114, 1129, 2017.

46.

Verheijen

, Lienhard

, Schrooders

, et al. DMSO induces drastic changes in human cellular processes and epigenetic landscape in vitro. Sci Rep, 9, 1, 2019.

47.

Puca

, Bareja

, Prandi

, et al. Patient derived organoids to model rare prostate cancer phenotypes. Nat Commun, 9, 1, 2018.

48.

Roe

J.S.

, Hwang

Il., Somerville, T.D.D., et al. Enhancer reprogramming promotes pancreatic cancer metastasis. Cell, 170, 875, 2017.

49.

McCracken

K.W.

, Catá

E.M.

, Crawford

C.M.

, et al. Modelling human development and disease in pluripotent stem-cell-derived gastric organoids. Nature, 516, 400, 2014.

50.

Crespo

, Vilar

, Tsai

S.Y.

, et al. Colonic organoids derived from human induced pluripotent stem cells for modeling colorectal cancer and drug testing. Nat Med, 23, 878, 2017.

51.

Romero-Calvo

, Weber

C.R.

, Ray

, et al. Human organoids share structural and genetic features with primary pancreatic adenocarcinoma tumors. Mol Cancer Res, 17, 70, 2019.

52.

Sachs

, de Ligt

, Kopper

, et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell, 172, 373, 2018.

53.

Bian

, Repic

, Guo

, et al. Erratum to: genetically engineered cerebral organoids model brain tumor formation (Nature Methods, (2018), 15, 8, (631–639), 10.1038/s41592-018-0070-7). Nat Methods, 15, 748, 2018.

54.

Mullenders

, de Jongh

, Brousali

, et al. Mouse and human urothelial cancer organoids: a tool for bladder cancer research. Proc Natl Acad Sci U S A, 116, 4567, 2019.

55.

Gao

, Vela

, Sboner

, et al. Organoid cultures derived from patients with advanced prostate cancer. Cell, 159, 176, 2014.

56.

Ogawa

, Pao

G.M.

, Shokhirev

M.N.

, et al. Glioblastoma model using human cerebral organoids. Cell Rep, 23, 1220, 2018.

57.

Kathuria

, Lopez-Lengowski

, Jagtap

S.S.

, et al. Transcriptomic landscape and functional characterization of induced pluripotent stem cell-derived cerebral organoids in schizophrenia. JAMA Psychiatry, 77, 745, 2020.

58.

Kathuria

, Lopez-Lengowski

, Vater

, et al. Transcriptome analysis and functional characterization of cerebral organoids in bipolar disorder. Genome Med, 12, 34, 2020.

59.

Wang

, Mokhtari

, Pedrosa

, et al. CRISPR/Cas9-mediated heterozygous knockout of the autism gene CHD8 and characterization of its transcriptional networks in cerebral organoids derived from iPS cells. Mol Autism, 8, 11, 2017.

60.

Dang

, Tiwari

S.K.

, Lichinchi

, et al. Zika virus depletes neural progenitors in human cerebral organoids through activation of the innate immune receptor TLR3. Cell Stem Cell, 19, 258, 2016.

61.

Heo

, Dutta

, Schaefer

D.A.

, et al. Modelling Cryptosporidium infection in human small intestinal and lung organoids. Nat Microbiol, 3, 814, 2018.

62.

Kallman

, Capowski

E.E.

, Wang

, et al. Investigating cone photoreceptor development using patient-derived NRL null retinal organoids. Commun Biol, 3, 82, 2020.

63.

Guo

, Wang

, Ma

J.H.

, et al. Modeling retinitis pigmentosa: retinal organoids generated from the iPSCs of a patient with the USH2A mutation show early developmental abnormalities. Front Cell Neurosci, 13, 361, 2019.

64.

Verheijen

, Schrooders

, Gmuender

, et al. Bringing in vitro analysis closer to in vivo: studying doxorubicin toxicity and associated mechanisms in 3D human microtissues with PBPK-based dose modelling. Toxicol Lett, 294, 184, 2018.

65.

Schulte

L.N.

, Schweinlin

, Westermann

A.J.

, et al. An advanced human intestinal coculture model reveals compartmentalized host and pathogen strategies during Salmonella infection. mBio, 11, 1, 2020.

66.

Boj

S.F.

, Hwang

I.l., Baker, L.A., et al. Organoid models of human and mouse ductal pancreatic cancer. Cell, 160, 324, 2015.

67.

Aref

A.R.

, Campisi

, Ivanova

, et al. 3D microfluidic: ex vivo culture of organotypic tumor spheroids to model immune checkpoint blockade. Lab Chip, 18, 3129, 2018.

68.

Vlachogiannis

, Hedayat

, Vatsiou

, et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science (80-), 359, 920, 2018.

69.

Nakaoka

, Saito

, Shimamoto

, et al. Cluster microRNAs miR-194 and miR-215 suppress the tumorigenicity of intestinal tumor organoids. Cancer Sci, 108, 678, 2017.

70.

, Yan

, Sun

, et al. Esophageal adenocarcinoma–derived extracellular vesicle microRNAs induce a neoplastic phenotype in gastric organoids. Neoplasia, 19, 941, 2017.

71.

Diermeier

S.D.

, Chang

K.C.

, Freier

S.M.

, et al. Mammary tumor-associated RNAs impact tumor cell proliferation, invasion, and migration. Cell Rep, 17, 261, 2016.

72.

Zeöld

, Sándor

G.O.

, Kiss

, et al. Shared extracellular vesicle miRNA profiles of matched ductal pancreatic adenocarcinoma organoids and blood plasma samples show the power of organoid technology. Cell Mol Life Sci 2020 [Epub ahead of print]; DOI: https://doi.org/10.1007/s00018-020-03703-8.

73.

Vernon

, Lambert

, Meryet-Figuière

, et al. Functional miRNA screening identifies wide-ranging antitumor properties of miR-3622b-5p and reveals a new therapeutic combination strategy in ovarian tumor organoids. Mol Cancer Ther, 19, 1506, 2020.

74.

Jones

E.J.

, Matthews

Z.J.

, Gul

, et al. Integrative analysis of Paneth cell proteomic and transcriptomic data from intestinal organoids reveals functional processes dependent on autophagy. Dis Model Mech, 12, 2019 [Epub ahead of print]; DOI:10.1242/dmm.037069.

75.

Kuzmanov

, Wang

E.Y.

, Vanderlaan

, et al. Mapping signalling perturbations in myocardial fibrosis via the integrative phosphoproteomic profiling of tissue from diverse sources. Nat Biomed Eng, 4, 889, 2020.

76.

Čermák

, Gandalovičová

, Merta

, et al. High-throughput transcriptomic and proteomic profiling of mesenchymal-amoeboid transition in 3D collagen. Sci Data, 7, 1, 2020.

77.

Thul

P.J.

, Akesson

, Wiking

, et al. A subcellular map of the human proteome. Science (80-), 356, 820, 2017.

78.

Bojkova

, Klann

, Koch

, et al. Proteomics of SARS-CoV-2-infected host cells reveals therapy targets. Nature, 583, 469, 2020.

79.

Kim

D.K.

, Park

, Han

, et al. Molecular and functional signatures in a novel Alzheimer's disease mouse model assessed by quantitative proteomics. Mol Neurodegener, 13, 2, 2018.

80.

Geyer

P.E.

, Wewer Albrechtsen

N.J.

, Tyanova

, et al. Proteomics reveals the effects of sustained weight loss on the human plasma proteome. Mol Syst Biol, 12, 901, 2016.

81.

Doll

, Gnad

, and Mann

The case for proteomics and phospho-proteomics in personalized cancer medicine. Proteomics Clin Appl, 13, e1800113, 2019.

82.

, He

, Kuang

, et al. Proteomic comparison of 3D and 2D glioma models reveals increased HLA-E expression in 3D models is associated with resistance to NK cell-mediated cytotoxicity. J Proteome Res, 13, 2272, 2014.

83.

Tölle

R.C.

, Gaggioli

, and Dengjel

Three-dimensional cell culture conditions affect the proteome of cancer-associated fibroblasts. J Proteome Res, 17, 2780, 2018.

84.

Lee

S.Y.

, Park

S.B.

, Kim

Y.E.

, et al. iTRAQ-based quantitative proteomic comparison of 2D and 3D adipocyte cell models co-cultured with macrophages using online 2D-nanoLC-ESI-MS/MS. Sci Rep, 9, 1, 2019.

85.

Hale

L.J.

, Howden

S.E.

, Phipson

, et al. 3D organoid-derived human glomeruli for personalised podocyte disease modelling and drug screening. Nat Commun, 9, 1, 2018.

86.

Yue

, Lukowski

J.K.

, Weaver

E.M.

, et al. Quantitative proteomic and phosphoproteomic comparison of 2D and 3D colon cancer cell culture models. J Proteome Res, 15, 4265, 2016.

87.

Levin

V.A.

, Panchabhai

, Shen

, et al. Protein and phosphoprotein levels in glioma and adenocarcinoma cell lines grown in normoxia and hypoxia in monolayer and three-dimensional cultures. Proteome Sci, 10, 5, 2012.

88.

Labonia

G.J.

, Ludwig

K.R.

, Mousseau

C.B.

, et al. ITRAQ quantitative proteomic profiling and MALDI-MSI of colon cancer spheroids treated with combination chemotherapies in a 3D printed fluidic device. Anal Chem, 90, 1423, 2018.

89.

Lee

H.K.

, Velazquez Sanchez

, Chen

, et al. Three dimensional human neuro-spheroid model of Alzheimer's disease based on differentiated induced pluripotent stem cells. PLoS One, 11, 1, 2016.

90.

Rosenbluth

J.M.

, Schackmann

R.C.J.

, Gray

G.K.

, et al. Organoid cultures from normal and cancer-prone human breast tissues preserve complex epithelial lineages. Nat Commun, 11, 1, 2020.

91.

Al-Juboori

A.A.A.

, Ghosh

, Jamaluddin

M.F.

Bin., et al. Proteomic analysis of stromal and epithelial cell communications in human endometrial cancer using a unique 3D co-culture model. Proteomics, 19, 1, 2019.

92.

Cristobal

, van den Toorn

H.W.P.

, van de Wetering

, et al. Personalized proteome profiles of healthy and tumor human colon organoids reveal both individual diversity and basic features of colorectal cancer. Cell Rep, 18, 263, 2017.

93.

Ortega-Prieto

A.M.

, Skelton

J.K.

, Wai

S.N.

, et al. 3D microfluidic liver cultures as a physiological preclinical tool for hepatitis B virus infection. Nat Commun, 9, 1, 2018.

94.

Kuen

, Darowski

, Kluge

, et al. Pancreatic cancer cell/fibroblast co-culture induces M2 like macrophages that influence therapeutic response in a 3D model. PLoS One, 12, e0182039, 2017.

95.

Marx

A dream of single-cell proteomics. Nat Methods, 16, 809, 2019.

96.

Lundberg

, and Borner

G.H.H.

Spatial proteomics: a powerful discovery tool for cell biology. Nat Rev Mol Cell Biol, 20, 285, 2019.

97.

Sato

, Kawana

, Adachi

, et al. Spheroid cancer stem cells display reprogrammed metabolism and obtain energy by actively running the tricarboxylic acid (TCA) cycle. Oncotarget, 7, 33297, 2016.

98.

Fujisawa

, Takami

, Sasai

, et al. Metabolic alterations in spheroid-cultured hepatic stellate cells. Int J Mol Sci, 21, 3451, 2020.

99.

Vorrink

S.U.

, Ullah

, Schmidt

, et al. Endogenous and xenobiotic metabolic stability of primary human hepatocytes in long-term 3D spheroid cultures revealed by a combination of targeted and untargeted metabolomics. FASEB J, 31, 2696, 2017.

100.

Ramachandran

G.K.

, and Yeow

C.H.

Proton NMR characterization of intact primary and metastatic melanoma cells in 2D & 3D cultures. Biol Res, 50, 12, 2017.

101.

Palma

, Grande

, Luciani

A.M.

, et al. Metabolic study of breast MCF-7 tumor spheroids after gamma irradiation by ¹H NMR spectroscopy and microimaging. Front Oncol, 6, 105, 2016.

102.

Fan

T.W.M.

, El-Amouri

S.S.

, Macedo

J.K.A.

, et al. Stable isotope-resolved metabolomics shows metabolic resistance to anti-cancer selenite in 3D spheroids versus 2D cell cultures. Metabolites, 8, 40, 2018.

103.

, Lodi

, Konopleva

, et al. Three-dimensional leukemia co-culture system for in vitro high-content metabolomics screening. SLAS Discov, 24, 817, 2019.

104.

Choi

U.Y.

, Lee

J.J.

, Park

, et al. Oncogenic human herpesvirus hijacks proline metabolism for tumorigenesis. Proc Natl Acad Sci U S A, 117, 8083, 2020.

105.

Shah

, Fritz

J.V.

, Glaab

, et al. A microfluidics-based in vitro model of the gastrointestinal human-microbe interface. Nat Commun, 7, 1, 2016.

106.

Priyadarsini

, Sarker-Nag

, Rowsey

T.G.

, et al. Establishment of a 3D in vitro model to accelerate the development of human therapies against corneal diabetes. PLoS One, 11, 1, 2016.

107.

Coloff

J.L.

, Murphy

J.P.

, Braun

C.R.

, et al. Differential glutamate metabolism in proliferating and quiescent mammary epithelial cells. Cell Metab, 23, 867, 2016.

108.

Vermeersch

K.A.

, Wang

, Mezencev

, et al. OVCAR-3 spheroid-derived cells display distinct metabolic profiles. PLoS One, 10, 1, 2015.

109.

Jansen

, Jansen

, Neven

, et al. Remote sensing and signaling in kidney proximal tubules stimulates gut microbiome-derived organic anion secretion. Proc Natl Acad Sci U S A, 116, 16105, 2019.

110.

van Gorsel

, Elia

, and Fendt

S.M.

13C tracer analysis and metabolomics in 3D cultured cancer cells. Methods Mol Biol, 1862, 53, 2019.

111.

Aguilar

, Marin De Mas

, Zodda

, et al. Metabolic reprogramming and dependencies associated with epithelial cancer stem cells independent of the epithelial-mesenchymal transition program. Stem Cells, 34, 1163, 2016.

112.

Rodenhizer

, Gaude

, Cojocari

, et al. Erratum: a three-dimensional engineered tumour for spatial snapshot analysis of cell metabolism and phenotype in hypoxic gradients (Nature Materials (2015) 10.1038/nmat4482). Nat Mater, 15, 244, 2016.

113.

Palubeckaitė

, Crooks

, Smith

D.P.

, et al. Mass spectrometry imaging of endogenous metabolites in response to doxorubicin in a novel 3D osteosarcoma cell culture model. J Mass Spectrom, 55, e4461, 2020.

114.

Tucker

L.H.

, Hamm

G.R.

, Sargeant

R.J.E.

, et al. Untargeted metabolite mapping in 3D cell culture models using high spectral resolution FT-ICR Mass Spectrometry Imaging. Anal Chem, 91, 9522, 2019.

115.

Vidavsky

, Kunitake

J.A.M

.R., Diaz-Rubio

M.E.

, et al. Mapping and profiling lipid distribution in a 3D model of breast cancer progression. ACS Cent Sci, 5, 768, 2019.

116.

Feldman

, Mukha

, Maor

I.I.

, et al. Blimp1+ cells generate functional mouse sebaceous gland organoids in vitro. Nat Commun, 10, 1, 2019.

117.

Kozyra

, Johansson

, Nordling, Å., et al. Human hepatic 3D spheroids as a model for steatosis and insulin resistance. Sci Rep, 8, 1, 2018.

118.

Jones

D.T.

, Valli

, Haider

, et al. 3D growth of cancer cells elicits sensitivity to kinase inhibitors but not lipid metabolism modifiers. Mol Cancer Ther, 18, 376, 2019.

119.

Pisanu

M.E.

, Maugeri-Saccà

, Fattore

, et al. Inhibition of Stearoyl-CoA desaturase 1 reverts BRAF and MEK inhibition-induced selection of cancer stem cells in BRAF-mutated melanoma. J Exp Clin Cancer Res, 37, 318, 2018.

120.

Ahlf

D.R.

, Masyuko

R.N.

, Hummon

A.B.

, et al. Correlated mass spectrometry imaging and confocal Raman microscopy for studies of three-dimensional cell culture sections. Analyst, 139, 4578, 2014.

121.

Grabowski

, and Rappsilber

A primer on data analytics in functional genomics: how to move from data to insight? Trends Biochem Sci, 44, 21, 2019.

122.

Hasin

, Seldin

, and Lusis

Multi-omics approaches to disease. Genome Biol, 18, 83, 2017.

123.

Seidlitz

, Merker

S.R.

, Rothe

, et al. Human gastric cancer modelling using organoids. Gut, 68, 207, 2019.

124.

Guo

, Li

, et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res, 27, 967, 2017.

125.

Weeber

, Van De Wetering

, Hoogstraat

, et al. Preserved genetic diversity in organoids cultured from biopsies of human colorectal cancer metastases. Proc Natl Acad Sci U S A, 112, 13308, 2015.

126.

Fong

E.L.S.

, Toh

T.B.

, Lin

, et al. Generation of matched patient-derived xenograft in vitro-in vivo models using 3D macroporous hydrogels for the study of liver cancer. Biomaterials, 159, 229, 2018.

127.

Feaver

R.E.

, Cole

B.K.

, Lawson

M.J.

, et al. Development of an in vitro human liver system for interrogating nonalcoholic steatohepatitis. JCI Insight, 1, e90954, 2016.

128.

Blandin

A.F.

, Durand

, Litzler

, et al. Hypoxic environment and paired hierarchical 3D and 2D models of pediatric H3.3-mutated gliomas recreate the patient tumor complexity. Cancers (Basel), 11, 1875, 2019.

129.

Wang

, Chen

, Liu

, et al. Engineering EMT using 3D micro-scaffold to promote hepatic functions for drug hepatotoxicity evaluation. Biomaterials, 91, 11, 2016.

130.

Hart

P.C.

, Kenny

H.A.

, Grassl

, et al. Mesothelial cell HIF1α expression is metabolically downregulated by metformin to prevent oncogenic tumor-stromal crosstalk. Cell Rep, 29, 4086.e6, 2019.

131.

Arai

, Sakamoto

, Kubota

, et al. Proteomic approach toward molecular backgrounds of drug resistance of osteosarcoma cells in spheroid culture system. Proteomics, 13, 2351, 2013.

132.

Tian

, Zhang

, Zou

, et al. Anticancer drug affects metabolomic profiles in multicellular spheroids: studies using mass spectrometry imaging combined with machine learning. Anal Chem, 91, 5802, 2019.

133.

Pamies

, Block

, Lau

, et al. Rotenone exerts developmental neurotoxicity in a human brain spheroid model. Toxicol Appl Pharmacol, 354, 101, 2018.

134.

Ishikawa

, Matsumura

, Kitamura

, et al. Multi-omics analysis: repeated exposure of a 3D bronchial tissue culture to whole-cigarette smoke. Toxicol In Vitro, 54, 251, 2019.

135.

Haswell

L.E.

, Baxter

, Banerjee

, et al. Reduced biological effect of e-cigarette aerosol compared to cigarette smoke evaluated in vitro using normalized nicotine dose and RNA-seq-based toxicogenomics. Sci Rep, 7, 888, 2017.

136.

Elias

M.S.

, Wright

S.C.

, Nicholson

W.V.

, et al. Functional and proteomic analysis of a full thickness filaggrin-deficient skin organoid model. Wellcome Open Res, 4, 134, 2019.

137.

Braun

L.M.

, Lagies

, Klar

R.F.U.

, et al. Metabolic profiling of early and late recurrent pancreatic ductal adenocarcinoma using patient-derived organoid cultures. Cancers (Basel), 12, 1440, 2020.

138.

, Sanderson

S.M.

, Zessin

, et al. Exercise inhibits tumor growth and central carbon metabolism in patient-derived xenograft models of colorectal cancer. Cancer Metab, 6, 14, 2018.

139.

Kang

Y.P.

, Yoon

J.H.

, Long

N.P.

, et al. Spheroid-induced epithelial-mesenchymal transition provokes global alterations of breast cancer lipidome: a multi-layered omics analysis. Front Oncol, 9, 145, 2019.

140.

Worthington Biochemical Corporation. 2020. Cell Isolation Optimizing System, Available at www.worthington-biochem.com/CIT/CIT_Product%20Insert_2012.pdf on October 1, 2020.

141.

Dutta

, Heo

, and Clevers

Disease modeling in stem cell-derived 3D organoid systems. Trends Mol Med, 23, 393, 2017.

142.

Zhao

, Rafatian

, Feric

N.T.

, et al. A platform for generation of chamber-specific cardiac tissues and disease modeling. Cell, 176, 913, 2019.

143.

Berkers

, Van Mourik

, Vonk

A.M.

, et al. Rectal organoids enable personalized treatment of cystic fibrosis. Cell Rep, 26, 1701, 2019.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.16 MB

0.00 MB