Secondary Logo

Journal Logo

Featured Articles

A Guide to Understanding “State-of-the-Art” Basic Research Techniques in Anesthesiology

Obal, Detlef MD, PhD, DESA*,†; Wu, Shaogen MD, PhD*; McKinstry-Wu, Andrew MD; Tawfik, Vivianne L. MD, PhD*

Author Information
doi: 10.1213/ANE.0000000000004801
  • Free



Figure 1.
Figure 1.:
NGS overview. (1) Preparation of samples: Multiple different types of samples can be processed to obtain material for NGS. RNA is extracted from bulk, LCM or FACS tissues. (2) Library construction: RNA is reverse transcribed to cDNA for stability and then fragmented and tagged with specific sequences that allow for batch processing of samples. (3) NGS: Samples are loaded onto a specialized flow cell where amplification and sequencing will take place. Cluster generation forms millions of “DNA colonies” and sequencing-by-synthesis begins to record the sequence of base pairs in each fragment. (4) Bioinformatic analysis: Once sequences are generated, they are aligned to a reference genome, normalized and process for quality control. A host of different analyses can then be applied depending on the outcome of interest. Readers are encouraged to access publicly available NGS datasets and bioinformatics resources for further information. cDNA indicates complementary DNA; CPM, counts per million reads mapped; FACS, fluorescence-activated cell sorting; FPKM: fragments per kilobase of transcript per million mapped reads; LCM, laser-capture microdissected; NGS, next-generation sequencing; RPKM, reads per kilobase of transcript per million mapped reads; TPM, transcripts per million reads mapped.

In 1977, Sanger et al1 developed a sequencing technique based on the incorporation of terminal dideoxynucleotides that expedited the sequencing of short DNA segments. “Sanger sequencing” has been widely used to identify gene mutations as direct causes or susceptibility factors associated with the development of genetic disorders; however, sequencing occurs 1 DNA fragment at a time, limiting scalability. During the past decade, next-generation sequencing (NGS) approaches using sample multiplexing (multiple samples in 1 sequencing run) have been developed that allow for high-throughput screens for genomic mutations and quantification of gene expression in tissue or cells from a disease of interest.2 Unlike previous assays, NGS techniques directly read sequences of a pool of gene fragments allowing for the detection of novel transcripts, providing unbiased and comprehensive genomic coverage.3 As a result, NGS approaches have truly revolutionized discovery in a huge variety of science fields (see Figure 1).


RNA sequencing (RNA-seq) is the dominant NGS approach used in basic and clinical research. In this review, we will focus on RNA-seq to illustrate the workflow of an NGS-based approach and discuss its advantages and limitations. The overall workflow consists of 4 parts: (1) preparation of samples, (2) library construction, (3) NGS, and (4) bioinformatic analysis.

Preparation of Samples

The starting materials for RNA-seq study are diverse including fresh clinical samples, laser-capture microdissection (LCM)–excised sections from formalin-fixed paraffin-embedded (FFPE) tissue, or cells obtained using fluorescence-activated cell sorting (FACS). A standard protocol for tissue collection and RNA purification is critical for successful transcriptomic profiling because the degradation of RNA can introduce biological bias.4 Collecting fresh tissue or cells in a commercially available RNA stabilization solution is a practical way to prevent RNA degradation. Newer “single-cell” approaches dissociate tissue into single-cell suspensions that allow the transcriptome of individual cells to be determined. However, these protocols can also cause cellular stress and transcriptomic changes,5,6 a fact that must be taken into account when designing and interpreting such studies.

Library Construction

This step converts the sample into a sequencing library that can be sequenced (read) on an NGS instrument. The majority of RNA-seq first requires the preparation of a complementary DNA (cDNA) library for stability. The selection of a specific protocol for subsequent library construction depends on the purpose of the study (eg, a ribosomal RNA depletion protocol allows detection of long noncoding RNAs that lack poly-A tails).7 In general, library preparation involves random fragmentation of the cDNA sample into shorter segments that can be reliably sequenced then followed by ligation of specialized 3′ and 5′ adapters that barcode samples and bind to the NGS flow cell (see below).8

Next-Generation Sequencing

Out of the many types of NGS techniques in the market,9 the most popular one is termed “sequencing by synthesis.”10 Generally speaking, each library, consisting of cDNA fragments labeled with adapters, is loaded onto a specialized flow cell with several lanes where amplification and sequencing will take place. Along each lane, there are millions of complementary oligonucleotides that anchor the libraries to the flow cell. Once the fragments have attached, a phase called cluster generation begins to form millions of “DNA colonies.” Next, primers and DNA polymerase start adding 1 fluorescently tagged deoxynucleoside triphosphate (dNTP) base,11 and in each round of synthesis, the sequencer records the base added to each fragment on the flow cell in parallel.

There are 3 major configurations of NGS which should be carefully chosen depending on the study purpose: read length, strand specificity, and sequencing depth. Read length refers to how many nucleotides can be read from a given library fragment. Longer reads can increase the specificity but depend on sequencer type and may cost more.9 Paired-end sequencing allows for both ends of the DNA fragment to be sequenced and can increase precision at splicing junctions,12 repetitive areas of the genome, or other difficult-to-sequence segments. Strand specificity refers to sequencing that retains the orientation of the transcripts from 5′ to 3′ end. This technique further increases the accuracy of the results by ensuring that reads are mapped to the correct gene and not a similar gene going in the opposite direction.13 Sequencing depth refers to how many reads (fragments) are sequenced per sample. A total read depth of 10 to 30 million reads per sample is considered “deep” enough for the coverage of larger transcriptomes such as human or mouse to ensure that low abundant transcripts are detected.9

Bioinformatic Analysis

The output of the NGS process will be millions of sequences that are generated, processed, and assigned to each sample, resulting in sequence data on the order of gigabytes. The first step of RNA-seq data analysis should be the assessment of the raw sequences. FastQC is a quality control (QC) tool which provides an overview of raw RNA-Seq data (Babraham Bioinformatics, To fur -ther improve the RNA-seq data quality, tools (eg, Trimmomatic, can be used for trimming and removal of library adapters that were placed for binding to the flow cell.14 After QC, sequenced reads are mapped, that is, aligned, to a reference genome or to a transcriptome database. The mapped reads are then counted and normalized for differential expression. Fragments per kilobase of transcript per million mapped reads (“FPKM”)/reads per kilobase of transcript per million mapped reads (“RPKM”) or counts per million reads mapped (“CPM”) are some of the units used to calculate the abundance of each gene expressed in a sample using different algorithms.15 Transcripts per million (“TPM”) is now becoming popular, which makes it easier to compare the proportion of reads that are mapped to a gene in each sample as it first normalizes for gene size followed by read depth.16,17

The most powerful use of RNA-seq is in finding differentially expressed genes (DEGs) between ≥2 conditions. There are many tools that perform differential expression calculations including DESeq, edgeR, and Limma-Voom, all of which are available through R/Bioconductor.18 Most tools use regression or nonparametric statistics to calculate a false discovery rate (FDR) for multiple hypotheses and identify DEGs by log fold change and statistical significance. To obtain a higher-level biological understanding of a list of DEGs, genes can be annotated using Gene Ontology (GO)19 or Kyoto Encyclopedia of Genes and Genomes (KEGG) terms,20 which categorize genes based on the family they belong to (eg, “inflammatory response” or “immune system process”). Downstream gene set enrichment analyses (GSEA) can then determine whether there are certain pathways or terms (eg, “circadian rhythms” or “platelet activation”) that are overrepresented in experimental versus control samples.21 Notably, there are many useful web-based NGS analysis tools available for analysis and visualization of large genomic data (see Figure 3). However, we recommend consultation with an experienced bioinformatician to ensure that appropriate QC is performed, to reduce the risk of bias, and to increase the reproducibility of the results.


NGS techniques, such as RNA-seq, have some clear advantages over older approaches including higher throughput, increased accuracy, ability to detect novel transcripts, higher sensitivity and specificity, and ability to detect low-abundance transcripts.22,23 NGS also seems to be the most cost-efficient tool for genome/transcriptome study nowadays. For example, sequencing the entire human genome by NGS now costs no more than $1000, compared to $100 million in 2001. However, NGS has clear limitations that must be taken into account when designing and interpreting experiments. First, transcription and translation occur at variable rates as does degradation of mRNA and protein. As a result, RNA levels may not necessarily reflect the actual level or activity of proteins of interest. This highlights the need for validation of DEGs at the protein level after RNA-seq profiling. Second, methods of sample preparation may trigger certain cellular pathways (eg, stress responses) which may be falsely overrepresented in the final dataset.24 Third, batch effects may skew results and experiments need to be carefully designed to mitigate them. Finally, NGS requires a large platform and supercomputer, which might not be easily accessible to all researchers. Importantly, several NGS sequencing services have recently entered the market at a reasonable cost and can be a good choice if internal core facility access is limited.


In clinical anesthesiology, NGS can be used for the identification of biomarkers for intervention, treatment outcome prediction, and understanding disease susceptibility, among other applications.25,26 For example, Tsalik et al25 sequenced peripheral blood RNA of 129 representative subjects with systemic inflammatory response syndrome (SIRS) or sepsis. They found that the expression of 338 genes differed between subjects with SIRS and those with sepsis and the expression of 1238 genes differed with sepsis outcome (survival versus nonsurvival).25 They also discovered that the expression of a gene called VPS9D1, which may control cell signaling through endocytosis of intracellular receptors, increased in sepsis survivors, who also expressed a higher number of missense variants in this gene.25 Another RNA-seq study relevant to our specialty examined ischemic changes, induced by cold blood cardioplegia on the left ventricular myocardium in 45 patients undergoing aortic valve replacement.26 Through transcriptomic analysis, 1241 DEGs were identified when comparing baseline samples to those obtained 79 minutes after aortic cross-clamping.26 Further functional study of these candidate genes may provide greater insight into the pathophysiology of ventricular ischemia that will guide the development of cardioprotective strategies.26


Many different NGS variants (eg, single-cell RNA-seq,27,28 spatial transcriptomics,29,30 and 16S rRNA sequencing for microbiome study)31 have been developed. These novel NGS techniques allow researchers to look at the transcriptome heterogeneity and the spatial arrangement of cell types in a given tissue. Using NGS, we are able to uncover new genes of interest but also to cluster DEGs within key pathways that may be targeted for improved therapeutic efficacy. Furthermore, as additional biomarkers are identified, there is power to monitor disease progression and treatment outcomes. NGS also provides a cost-efficient tool for personalized medicine approaches by identifying whole transcriptome signatures that may allow for mechanism-based treatments. With the development of standardized laboratory protocols and bioinformatics pipelines, NGS is now becoming more accessible for use in both basic and clinical research. Finally, several publicly available, user-friendly NGS data repositories exist that can be interrogated for hypothesis development or used to cross-reference new datasets.32 Overall, NGS technologies have already contributed new knowledge to our field, and with further adoption by anesthesiologist scientists, there lies the huge potential for furthering our impact on a diverse set of disease-specific questions.


Precise genome editing has become an indispensable tool for research programs incorporating animal models and is increasingly finding use in translational research. The clustered regularly interspaced short palindromic repeat (CRISPR) toolset is a highly adaptable, easily implemented approach for changing a genome at a precise, specified location (ie, directed recombination). Anesthesia research has incorporated gene editing using various techniques for decades,33–36 and as CRISPR has become the predominant method of editing, it has become central to many anesthesia research endeavors. Uses for this versatile toolset have already extended to include gene regulation, single-base editing, and inducible recombination. The future will undoubtedly see a continued expansion of CRIPSR technology with innovations such as conditional or inducible single-nucleotide editing on the horizon.

While genetic recombination in mammalian models was pioneered nearly 35 years ago,37 the feasibility and adoption of this approach have dramatically increased over the past decade. Early genetic editing relied on rare spontaneous recombination events to incorporate introduced genetic material after untargeted double-stranded DNA (dsDNA) breaks at random locations. Eukaryotes use 2 separate processes to repair such breaks: homology-directed DNA repair (HDR) or nonhomologous end joining (NHEJ). NHEJ is the faster and more common repair mechanism and involves directly ligating 2 broken ends of DNA together without the use of any DNA template. This type of repair, as it does not have a template to error check, can result in insertions or deletions at the break site. HDR, in contrast, does use a DNA template to facilitate and error check the repair process (Figure 2A).38 By introducing template DNA with a desired change rather than the native genome, which would normally serve as a template, HDR can be used to specifically alter genomic DNA.

Figure 2.
Figure 2.:
CRISPR/Cas9 gene-editing and next-generation CRISPR technologies. A, sgRNA associated with Cas9 pairs with a complementary genomic sequence that is 5′ to a PAM. Because of the genomic target sequence’s proximity to the PAM, the Cas9 cleaves the DNA, creating a DSB. Repair of the DSB occurs through 1 of 2 endogenous processes, NHEJ or HDR. NHEJ can result in short insertions or deletions (indels) of sequence at the break site. HDR uses template DNA; if exogenous template DNA is provided that is homologous to the target sequence with a desired sequence inserted at the break site, this directed insertion can be incorporated into the genomic DNA. B, Potential next-generation CRISPR technology that has yet to be implemented includes inducible (top) and conditional (bottom) single-base editing without DSB. The example given is a method of inducible base pair editing using a tetracycline-dependent promoter governing Cas9 nickase fused with a deaminase and guide RNA. The bottom example of conditional single-base editing is the same nCas9–deaminase and sgRNA in reverse orientation with flanking LoxP sites, requiring the presence of Cre recombinase to flip the orientation of the sequence and allow for transcription and translation. Cas9 indicates CRISPR-associated protein 9; CRISPR, clustered regularly interspaced short palindromic repeat; DSB, double-stranded DNA break; HDR, homology-directed repair; LoxP, 34 bp sequence where Cre recombinase binds; nCas9, Cas9 nickase; NHEJ, nonhomologous end joining; PAM, protospacer adjacent motif; rtTA, reverse tetracycline-controlled transactivator; sgRNA, single-guide RNA; tet, tetracycline; TRE, tetracycline-responsive element.

Relying on spontaneous recombination on nontargeted breaks is extremely inefficient with a high chance of gene incorporation at off-target sites.39,40 The ability to specifically target dsDNA cleavage increases both the reliability and specificity of genome editing. This is despite most double-stranded breaks being repaired using NHEJ rather than the desired template-driven HDR.38 The ability to adaptably target DNA breaks expanded the adoption of genome editing due to an increased rate of recombination at desired loci. Targeted breaks were first produced using mega-nucleases, but poor flexibility in target sequence selection of these large proteins caused them to be replaced by chimeric proteins fusing DNA recognition domains with the DNA cleavage domain of the endonuclease Fok I.41,42 This technique requires engineering new chimeric proteins for each new sequence to be targeted. The technical hurdles involved in engineering and cloning novel proteins limited the adoption of techniques using this strategy.


CRISPRs major advantages over its gene-editing predecessors are its simplicity and flexibility, while it largely matches the efficacy and efficiency of earlier techniques. The crucial elements of the CRISPR toolbox are derived from an endogenous bacterial antiviral process. In this bacterial system, 2 short CRISPR RNA sequences (crRNAs)—one of which includes a 17–28 nucleotide portion homologous to bacteriophage sequences—form complexes with CRISPR-associated proteins (Cas) to target the viral sequence encoded by the crRNA.43 When such a complex incorporates a DNA-cleaving Cas element, such as Cas9, the result is a targeted double-stranded DNA break (DSB) with the target specified by an RNA sequence. For the CRISPR-Cas complex to attach and cleave the viral DNA, the targeted viral genomic sequence must include a short protospacer adjacent motif (PAM), a 3–7 nucleotide long sequence specific to a given Cas enzyme, 3' to the targeted sequence. Though these systems are native to bacteria and are intended to target viral DNA sequences, with modifications to Cas9 to include a nuclear localization sequence, they are able to modify mammalian genomic DNA as well.44–46 Another early modification to the system was the substitution of a single-guide RNA (sgRNA) for the 2 crRNAs, further reducing complexity.47 The modular nature of the system, with interchangeable Cas protein elements (for enzymatic activity) and sgRNAs (for target sequence selection) combined with the comparative ease of nucleotide synthesis, makes this system an extremely useful biotechnology tool, both flexible in its application, and simple enough to implement that it can be readily adopted without extensive technical prerequisites.

CRISPR/Cas9 was the first widely adopted CRISPR toolset for gene editing. Despite its simplicity and flexibility, this approach is not without its drawbacks. While RNA-mediated targeting for CRISPR/Cas9 is flexible and easily changed, genomic locations for cleavage are limited by the necessity of a PAM sequence 3′ to the cleavage site. PAM sequences are short and occur frequently enough within the genome that this restriction rarely presents a major impediment, but it is a limitation. Like their predecessors, zinc-finger nucleases and transcription activator-like effector nucleases (TALENs), CRISPR/Cas9 systems make use of endogenous DNA repair processes after DSBs. The 2 separate events—the initial DSB and subsequent repair—are each possible sources of error. Though targeting is specified with PAMs and the 17–20 nucleotide sequence in crRNA, careful examinations of off-target effects show that CRISPR systems display some flexibility in their targeting of the sequence, causing DSBs at sites not predicted by sequence alone.48,49 These off-target cleavages can produce insertions and deletions at disparate sites within the genome. Even when a DSB occurs in the correct location, NHEJ, with its associated insertions of random genetic material or deletions of proximate sequence, occurs 8 times more frequently than the more desirable HDR.50 Numerous improvements have made to CRISPR/Cas genome-editing systems, largely focused on increased targeting flexibility, decreased off-target cutting, and increased rate of HDR over NHEJ after a cut. Increased targeting flexibility through altering PAM sequences is a significant source of innovation. The most commonly used Cas9—derived from Streptococcus pyogenes—has 3 bp sequence with any nucleotide (N) followed by 2 guanines (GG) (NGG) as its PAM sequence, but Cas derived from other species have alternate sequences, expanding potential targets within a genome. In addition to naturally occurring homologs, engineered Cas variants can have altered, and even more flexible, PAM requirements.51–53 Diverse strategies have been successful at decreasing the rate of off-target DSBs, another goal for innovation. Cas variants specifically engineered to decrease the rate of off-target cutting,54 altering sgRNA length, or delivering the complete sgRNA–Cas ribonucleoprotein complex (rather than a plasmid encoding both) have all succeeded at reducing off-target cutting.55,56 Using Cas nickases, which cut only 1 of the 2 DNA strands, and 2 sgRNAs independently targeting sequences directly opposite each other, 2 cuts need to independently occur to create a DSB, significantly reducing the rate of off-target DSBs.57 Finally, temporal and spatial control of enzymatic activity using light- or small-molecule-activated Cas can also decrease off-target cutting.58,59 Similarly, diverse approaches have been deployed toward increasing the rate of template-driven HDR and decreasing the incidence of NHEJ after producing a DSB. The rate of HDR can be increased using either small molecule adjuvants or single-stranded DNA templates.60,61 Certain Cas variants produce staggered DSBs in DNA, leaving overhanging sticky ends. These staggered breaks have a higher rate of HDR, such that use of this class of Cas, including Cpf1, can increase the rate of successful recombination.62

Applications of CRISPR-based systems have expanded beyond simple facilitated recombination using targeted DSBs. Fusing catalytically inactive Cas9 (dCas9) to other functional domains has emerged as a flexible means of targeting specific loci in the genome for a variety of purposes. These include attaching gene regulatory elements to dCas9 to enhance or inhibit transcription,63,64 fluorescent tags to visualize gene locations in the nuclei of live cells,65 and methyltransferase to alter local DNA methylation.66 Newer techniques have opened the door to gene editing without creating DSBs. The fusion of Cas nickase with various deaminases allows the direct conversion of C·G > T·A or A·T > G·C and removal of the opposite paired base such that, by targeting the correct strand, any single genomic base pair can be converted to any other.67,68 This technology is still in its early stages, however, and deaminases can induce significant off-target mutations.69 The potential for such “second-generation” CRISPR-based gene editing nevertheless has yet to be fully realized. Specifically, approaches that have been previously used in conjunction with first-generation CRISPR gene-editing technology, inducible and conditional expression, could be applied to second-generation systems to produce cell-line-specific or inducible targeted single-base mutations (Figure 2B) Such limited targeted mutations could allow for a range of studies on critical genes that otherwise prove lethal when altered during development.


The use of CRISPR technology for gene editing has only become widely adopted over the past 5 years, and thus we are just beginning to see studies using CRISPR-engineered mutants in the anesthesiology literature.70 As CRISPR-based gene-editing and advanced CRISPR techniques become the foundation for a wide variety of investigations in the science and practice of anesthesiology, it will become ever more critical for anesthesiologists to understand many of the previously mentioned hazards and potential limitations of the technique, in addition to appreciating the diverse applications. In interpreting these types of studies, it is important that readers look for appropriate controls accounting for potential off-target cutting including insertions and deletions. Alternatively, the study authors may include measures to minimize the impact of such events such as backcrossing animals to a wild-type background or the use of enzymes or adjuvants that minimize such off-target events.


CRISPR-based technologies have significantly lowered the barrier to entry for gene-editing approaches, and new applications offer similar potential for gene regulation and epigenomic approaches. CRISPR has already broadened the types of basic science questions that can be asked by opening the door to the use of new model organisms.71 Separately, second-generation editing has the potential to bridge gene-editing technologies into the clinical realm in the coming years through precise and limited genomic alterations. We have yet to see the full scope of the impact of these powerful and flexible approaches, but continued refinement with reduction of off-target effects will be necessary before this technology can move from the bench to the bedside.


Human embryonic stem cells (hESCs) are derived from the inner cell mass of fresh or frozen embryos at the blastocyst stage of development.72 Embryonic stem cells (ESCs) are self-renewable and able to give rise to cells of all 3 germ layers (ectoderm, endoderm, and mesoderm).73,74 Indefinite replication makes hESCs a valuable tool to study anesthetic mechanisms in human tissue. However, ethical controversies and limited supply of donor human embryos restricted the use of this cell type and alternative approaches were warranted.

In 2006, Takahashi and Yamanaka75 performed an experiment in which they selected 24 different transcription factors as candidates to induce pluripotency in somatic cells, that is, reprogramming of already differentiated cells into ESC-like cells. After extensive screening, they identified 4 transcription factors essential to produce ESC-like colonies able to form teratoma: octamer-binding transcription factor 2 and 4 (Oct2/4), SRY (sex determining region Y)-box 2 (Sox2), Krüppel-like factor 4, (Klf4), and the oncogene cMYC (c-Myc) (also known as “OSKM”).75 These inducible pluripotent stem cells (iPSCs) have become a valuable tool in basic science, and only 6 years after his original observation, Shinya Yamanaka (together with John B. Gurdon) received the Nobel Prize in Physiology or Medicine for their “discovery of reprogramming mature cells into pluripotent stem cells.”

The great potential of this technique relies on its ability to generate any somatic cell line out of an indefinitely dividing stem cell pool, specific to a particular patient and collectable without invasive procedures. In other words, peripheral human blood cells can be reprogrammed into stem cells which will be subsequently differentiated into a somatic cell of any type.76–81 With regard to morphology, surface marker expression, global gene expression profile, DNA methylation status, and other characteristics, human iPSC-derived cell (hiPSC) and hESC are considered to be similar.82 Herein we will focus on hiPSC-derived somatic cells, which are of particular interest for anesthesiologists: cardiomyocytes and neurons (Figure 3). Although hiPSC-derived somatic cells are frequently cultured in monolayers (2-dimensional [2D] culture) when used for drug and phenotype testing, our description will concentrate on hiPSC-derived cardiomyocyte (hiPSC-CM) and neuronal cell (hiPSC-NC) tissues grown as organoids (ie, 3D structures), which are build out of different cell types to better reflect the “natural” original.

Figure 3.
Figure 3.:
Inducible pluripotent stem cells overview. Human cardiac and brain tissue is difficult to access and usually not available for drug testing. After reprogramming mature, somatic cells via induction of 4 transcription factors (Yamanaka factors, Oct4, Sox2/4, Klf4, and Myc) into iPSC can be differentiated by modifying cell culture conditions and adding cell-type specific differentiation factors or small molecules into cardiovascular tissue (EC, CM, CFB) or neuronal tissue (Neuro, AstrC, OligoD, PeriC, MG). Complexity of development and testing increases with the development of organoids or complex engineered tissues. Each tissue type can be subject to functional, morphological, or genetic studies. AstrC indicates astrocytes; CFB, cardiac fibroblasts; CM, cardiomyocytes; EC, endothelial cell; iPSC, inducible pluripotent stem cells; Klf4, Krüppel-like factor 4, transcription factor; MG, microglia cells; Myc, MYC proto-oncogene, transcription factor; Neuro, neurons; Oct4, octamer-binding transcription factor 4; OligoD, oligodendroglia cells; PeriC, pia cells; scRNA-seq, single-cell RNA sequencing; Sox2/4, SRY (sex determining region Y)-box 2 transcription factor.

To build organoids based on patients’ own cells has been a long-term objective within the iPSC community; however, the organoids should reflect “structure” and “cell composition” of their natural counterparts. Nowadays, structure modifications have been archived by applying different bioengineering methods83,84 and by modifying cell culture conditions.85,86 Diversity of cell composition within the organoids has been realized in 2 different ways: human iPSC/ESC have been differentiated by stepwise adding small molecules and growth factors resulting in subsequent development of various cell types reflecting “naturally” formed organoids.87 Alternatively, hiPSCs have been differentiating into multiple cell types separately and then in a subsequent step “assembled” to the final organoid. While the first method allows to study “natural” progression of cell differentiation during organogenesis, the second method allows to control for cell-type composition within the organoids superior for description of cell–cell interactions.

Compared to 2D cell layers, hiPSC growing as 3D organoids have less contact with culture plates, thus favoring interaction between cells and maintaining histological and genetic complexity even after long-term culture.88 Importantly, cells grown in organoids have a distinct gene expression profile compared to their 2D counterparts resulting in contrasting pharmacological profiles. Luca et al89 demonstrated that tumor cells cultured in a 3D model express significantly less epithelial growth factor receptors (EGFRs, a factor important for controlling cell proliferation) compared to tumor cells cultured in 2D culture which made these cells less susceptible to EGFR tyrosine kinase inhibitors. As EGFR tyrosine kinase inhibitors are tested as chemotherapeutic agents, the therapeutic efficiency reported in different studies may deviate significantly and impact drug development.


By aligning hiPSC-CM in parallel rather than randomly within an extracellular matrix,90 Zimmermann et al,91 and Eschenhagen et al92 developed engineered heart tissue (EHT) models in which cardiomyocytes increased in size and started to form a connected syncytium. EHT allows advanced functional assessment of human tissue in a dish,92 even in a high-throughput screening format.93 The current end of these bioengineering developments constitutes a 3D electromechanically coupled, fluid-ejecting miniature ventricle derived from hiPSC-CM,94 in which anesthetic agents and other drugs might be tested under “physiological” conditions “in vitro” in the future.


HiPSC-derived cerebral organoids offer great potential to study the neurotoxic effects of anesthetics and their modes of action. Currently, the time to generate self-organized 3D structures spans from 50 to 100 days.95,96 The construction of neuronal organoids97,98 allows the prediction of anesthetic-induced neurotoxicity,99 and with advancements in single-cell technology, we have the tools to obtain transcriptomes of single cells and to cluster cells with identical transcriptional activities.100 With the combination of hiPSC-NC and vascular cells, neuronal-vascular units have been developed101,102 to study brain development103 or function and neurotoxicity of anesthesia-inducing drugs.104,105 Unfortunately, this technique is still underutilized in our specialty and during anesthetic and analgesic drug development.


Although hiPSC have been discovered >10 years ago, the technique is still evolving and some limitations should be considered. Retrieving cells from patients appears to be relative easy and straightforward, however, as the efficiency of reprogramming primary cells (skin fibroblast versus blood cells) depends on the cell type,106 which should therefore be carefully chosen.

Another consideration for using hiPSC technology in clinical practice may be the maturation status of hiPSC-derived cardiomyocytes or neurons. Although functional cardiomyocytes (ie, contracting cells, hiPSC-CM) develop within 7–9 days,107 an additional of 90–120 days may be required to generate cardiomyocytes with a more mature phenotype (ie, expression of adult genes, normal morphology, and expression of normal T-tubule and sarcomere structure).108 Similarly, differentiation hiPSC toward neuronal tissue requires between 2109–111 and 6 weeks.112–114 These culture requirements may currently not be suitable for “preoperative” bedside tests but may impact care for patients undergoing long-term planned elective, high-risk procedures or for patients with repeated anesthetic exposures (ie, pediatric patients after correction of congenital heart defects).

Additional concerns about hiPSCs genetic stability during reprogramming as well as differentiation-dependent variability and heterogeneity exist.115,116 These reservations may be justified; however, they reflect limitations of a relatively young technology which will be resolved within the coming years.


How do hiPSC-derived cells help anesthesiologists to understand cellular function and where are the limitations? By using a few assays as an example, advantages and limitations of iPSC-based techniques will be outlined.

One of the advantages of hiPSC-CM and hiPSC-NC is the ability to perform functional assessments of patient-specific tissue in vitro. Cardiac arrhythmias and QTc interval prolongation are significant milestones to overcome during drug development. With the development of hiPSC-CM, drug-induced QTc prolongation can be successfully studied in human tissue “in a dish.” hiPSC-CM of patients with inherited channelopathies can be easily generated from a 10-mL blood sample to study ion channel function or membrane potential changes by different methods (ie, patch clamp, fluorescence dyes, genetically introducing voltage, and/or calcium indicators117–120).

Most recently, hiPSC-CM has been used to determine the cardiac side profile of KSEB01-02, a new compound with hypnotic properties.121 Using a high-throughput screening method, KSEB01-02 turned out to be less cardio-depressant than propofol, elucidating the advantage of using hiPSC-CM to screen for cardiovascular side effects of a new drug in human heart tissue.

Similar methods can be utilized to evaluate communication within human neuronal networks. HiPSC-NC becomes electrically active within a few days of differentiation,122 and with new methods like “patch-seq,” it is possible to study simultaneously functional (patch clamp) and genetic (ie, single-cell RNA-seq) characteristics of single cells of a human neuronal network in vitro.123,124

Another advantage of hiPSC-CM and hiPSC-NC is the ability to measure cell toxicity of anesthetic agents. Propofol infusion syndrome is a life-threatening complication of long-term sedation with rather high doses of propofol.125 Nevertheless, the mechanism of cell toxicity was unclear on the cellular level. By treating hiPSC-CM with propofol (0–50 µg/mL) for 48 hours, Kido et al126 revealed that high propofol concentrations cause mitochondrial dysfunction by downregulating of peroxisome proliferator-activated receptor-γ-coactivator 1-α (PGC-1α) in human tissue. While the majority of neurotoxicity studies are based on nonhuman tissue, hiPSC-NC also offered an opportunity to study the impact of prolonged isoflurane exposure on neuronal survival and neurogenesis in human tissue.127 Interestingly, the anesthetic toxicity seems to be strongly dependent on cytosolic Ca2+ concentration within the hiPSC-NC.127

The molecular mechanism of some anesthetic actions are still unknown. Ketamine’s recent renaissance as antidepressant agent raised questions about the underlying mechanism of action: Collo et al128 differentiated hiPSCs into floor plate-derived midbrain dopaminergic neurons and demonstrated that the structural plasticity and expression of α-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptor (AMPAR) and their subunit GluR1 and GluR2 might be causative for positive, antidepressant effects of the drug.

While anesthetic mechanisms have been studied in nonhuman tissue samples or in large clinical trials, these examples show that with the new hiPSC technology a unique venue became available to study functional and toxicological effects of modern and future anesthetics in human tissues. By using patient-specific iPSC-derived tissues, personalized medications and therapeutic plans can be developed and transferred into clinical practice.


While iPSC technology has facilitated the development of new agents in cardiovascular medicine,129,130 neurology,131 and oncology,132 the use for development of new perioperative medications has been limited. Although anesthesia-related drugs have not changed dramatically over the past 50 years, the fascinating developments in molecular and cell-based techniques will pave the way toward improved anesthesia drugs focusing precisely on the individual patient. Not only will iPSC technologies allow the development of new compounds but also facilitate our understanding of current anesthesia medications. With the rapid progress in biochip technology, we will hopefully advance to generating testing platforms which will shorten the time to determine patient-specific personalized drug cocktails during the perioperative care period.


Basic research techniques are rapidly evolving and increasing in complexity and sophistication. Although anesthesia is considered safe, we should not ignore the opportunity to utilize such approaches to study diseases of relevance to our specialty, to increase our understanding on how anesthetic drugs function, and where possible, to move forward drug development opportunities and personalized medicine. This review will hopefully encourage others to describe their sophisticated research tools to (1) make them more understandable and relevant to the clinically practicing anesthesiologist and (2) to increase the interest and facilitate the use of these exciting novel techniques to improve anesthesia care in the future.


Name: Detlef Obal, MD, PhD, DESA.

Contribution: This author helped review the literature, design the figures, write the chapter on inducible pluripotent stem cell technology, design the concept of the review, and review the manuscript before submission.

Name: Shaogen Wu, MD, PhD.

Contribution: This author helped review the literature, design the figures, write the chapter on next-generation sequencing, and review the manuscript before submission.

Name: Andrew McKinstry-Wu, MD.

Contribution: This author helped review the literature, design the figures, write the chapter on clustered regularly interspaced short palindromic repeat technology, and review the manuscript before submission.

Name: Vivianne L. Tawfik, MD, PhD.

Contribution: This author helped review the literature, design the figures, write the chapter on next-generation sequencing, conceptualize the manuscript, and review the manuscript before submission.

This manuscript was handled by: Jean-Francois Pittet, MD.



    1. Sanger F, Nicklen S, Coulson ARDNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci USA. 1977;74:5463–5467.
    2. Morozova O, Marra MAApplications of next-generation sequencing technologies in functional genomics. Genomics. 2008;92:255–264.
    3. Roberts A, Pimentel H, Trapnell C, Pachter LIdentification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics. 2011;27:2325–2329.
    4. Reiman M, Laan M, Rull K, Sõber SEffects of RNA integrity on transcript quantification by total RNA sequencing of clinically collected human placental samples. FASEB J. 2017;31:3298–3308.
    5. van denBrink SC, Sage F, Vértesy Á, et al.Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat Methods. 2017;14:935–936.
    6. O’Flanagan CH, Campbell KR, Zhang AW, et alCRUK IMAXT Grand Challenge Team. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 2019;20:210.
    7. Zhao W, He X, Hoadley KA, Parker JS, Hayes DN, Perou CMComparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genomics. 2014;15:419.
    8. Hrdlickova R, Toloue M, Tian BRNA-Seq methods for transcriptome analysis. Wiley Interdiscip Rev RNA. 2017;8:1–11.
    9. Stark R, Grzelak M, Hadfield JRNA sequencing: the teenage years. Nat Rev Genet. 2019;20:631–656.
    10. Mardis ERNext-generation DNA sequencing methods. Annu Rev Genomics Hum Genet. 2008;9:387–402.
    11. Canard B, Sarfati RSDNA polymerase fluorescent substrates with reversible 3’-tags. Gene. 1994;148:1–6.
    12. Chhangawala S, Rudy G, Mason CE, Rosenfeld JAThe impact of read length on quantification of differentially expressed genes and splice junction detection. Genome Biol. 2015;16:131.
    13. Levin JZ, Yassour M, Adiconis X, et al.Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods. 2010;7:709–715.
    14. Bolger AM, Lohse M, Usadel BTrimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–2120.
    15. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold BMapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621–628.
    16. Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CNRNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2010;26:493–500.
    17. Wagner GP, Kin K, Lynch VJMeasurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 2012;131:281–285.
    18. Huber W, Carey VJ, Gentleman R, et al.Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12:115–121.
    19. Ashburner M, Ball CA, Blake JA, et al.Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–29.
    20. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa MKEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999;27:29–34.
    21. Subramanian A, Tamayo P, Mootha VK, et al.Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–15550.
    22. Wu S, Marie Lutz B, Miao X, et al.Dorsal root ganglion transcriptome analysis following peripheral nerve injury in mice. Mol Pain. 2016;12:1744806916629048.
    23. Baskozos G, Dawes JM, Austin JS, et al.Comprehensive analysis of long noncoding RNA expression in dorsal root ganglion reveals cell-type specificity and dysregulation after nerve injury. Pain. 2019;160:463–485.
    24. Haimon Z, Volaski A, Orthgiess J, et al.Re-evaluating microglia expression profiles using RiboTag and cell isolation strategies. Nat Immunol. 2018;19:636–644.
    25. Tsalik EL, Langley RJ, Dinwiddie DL, et al.An integrated transcriptome and expressed variant analysis of sepsis survival and death. Genome Med. 2014;6:111.
    26. Muehlschlegel JD, Christodoulou DC, McKean D, et al.Using next-generation RNA sequencing to examine ischemic changes induced by cold blood cardioplegia on the human left ventricular myocardium transcriptome. Anesthesiology. 2015;122:537–550.
    27. Tang F, Barbacioru C, Wang Y, et al.mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6:377–382.
    28. Usoskin D, Furlan A, Islam S, et al.Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat Neurosci. 2015;18:145–153.
    29. Ståhl PL, Salmén F, Vickovic S, et al.Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353:78–82.
    30. Maniatis S, Äijö T, Vickovic S, et al.Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science. 2019;364:89–93.
    31. Shen S, Lim G, You Z, et al.Gut microbiota is critical for the induction of chemotherapy-induced pain. Nat Neurosci. 2017;20:1213–1216.
    32. Crow M, Denk FRNA-seq data in pain research-an illustrated guide. Pain. 2019;160:1502–1504.
    33. Lakhlani PP, MacMillan LB, Guo TZ, et al.Substitution of a mutant alpha2a-adrenergic receptor via “hit and run” gene targeting reveals the role of this subtype in sedative, analgesic, and anesthetic-sparing responses in vivo. Proc Natl Acad Sci USA. 1997;94:9950–9955.
    34. Jurd R, Arras M, Lambert S, et al.General anesthetic actions in vivo strongly attenuated by a point mutation in the GABA(A) receptor beta3 subunit. FASEB J. 2003;17:250–252.
    35. Quintana A, Morgan PG, Kruse SE, Palmiter RD, Sedensky MMAltered anesthetic sensitivity of mice lacking Ndufs4, a subunit of mitochondrial complex I. PLoS One. 2012;7:e42904.
    36. Ramadasan-Nair R, Hui J, Zimin PI, Itsara LS, Morgan PG, Sedensky MMRegional knockdown of NDUFS4 implicates a thalamocortical circuit mediating anesthetic sensitivity. PLoS One. 2017;12:e0188087.
    37. Thomas KR, Folger KR, Capecchi MRHigh frequency targeting of genes to specific sites in the mammalian genome. Cell. 1986;44:419–428.
    38. West SCMolecular views of recombination proteins and their control. Nat Rev Mol Cell Biol. 2003;4:435–445.
    39. Capecchi MRAltering the genome by homologous recombination. Science. 1989;244:1288–1292.
    40. Lin FL, Sperle K, Sternberg NRecombination in mouse L cells between DNA introduced into cells and homologous chromosomal sequences. Proc Natl Acad Sci USA. 1985;82:1391–1395.
    41. Rouet P, Smih F, Jasin MIntroduction of double-strand breaks into the genome of mouse cells by expression of a rare-cutting endonuclease. Mol Cell Biol. 1994;14:8096–8106.
    42. Kim YG, Cha J, Chandrasegaran SHybrid restriction enzymes: zinc finger fusions to Fok I cleavage domain. Proc Natl Acad Sci USA. 1996;93:1156–1160.
    43. Adli MThe CRISPR tool kit for genome editing and beyond. Nat Commun. 2018;9:1911.
    44. Jinek M, East A, Cheng A, Lin S, Ma E, Doudna JRNA-programmed genome editing in human cells. Elife. 2013;2:e00471.
    45. Mali P, Yang L, Esvelt KM, et al.RNA-guided human genome engineering via Cas9. Science. 2013;339:823–826.
    46. Cong L, Ran FA, Cox D, et al.Multiplex genome engineering using CRISPR/Cas systems. Science. 2013;339:819–823.
    47. Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier EA programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science. 2012;337:816–821.
    48. Pattanayak V, Lin S, Guilinger JP, Ma E, Doudna JA, Liu DRHigh-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat Biotechnol. 2013;31:839–843.
    49. Tsai SQ, Zheng Z, Nguyen NT, et al.GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat Biotechnol. 2015;33:187–197.
    50. Miyaoka Y, Berman JR, Cooper SB, et al.Systematic quantification of HDR and NHEJ reveals effects of locus, nuclease, and cell type on genome-editing. Sci Rep. 2016;6:23549.
    51. Kleinstiver BP, Prew MS, Tsai SQ, et al.Broadening the targeting range of Staphylococcus aureus CRISPR-Cas9 by modifying PAM recognition. Nat Biotechnol. 2015;33:1293–1298.
    52. Kleinstiver BP, Prew MS, Tsai SQ, et al.Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature. 2015;523:481–485.
    53. Nishimasu H, Shi X, Ishiguro S, et al.Engineered CRISPR-Cas9 nuclease with expanded targeting space. Science. 2018;361:1259–1262.
    54. Tan Y, Chu AHY, Bao S, et al.Rationally engineered Staphylococcus aureus Cas9 nucleases with high genome-wide specificity. Proc Natl Acad Sci USA. 2019;116:20969–20976.
    55. Fu Y, Sander JD, Reyon D, Cascio VM, Joung JKImproving CRISPR-Cas nuclease specificity using truncated guide RNAs. Nat Biotechnol. 2014;32:279–284.
    56. Lin S, Staahl BT, Alla RK, Doudna JAEnhanced homology-directed human genome engineering by controlled timing of CRISPR/Cas9 delivery. Elife. 2014;3:e04766.
    57. Mali P, Aach J, Stranges PB, et al.CAS9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering. Nat Biotechnol. 2013;31:833–838.
    58. Nihongaki Y, Kawano F, Nakajima T, Sato MPhotoactivatable CRISPR-Cas9 for optogenetic genome editing. Nat Biotechnol. 2015;33:755–760.
    59. Dow LE, Fisher J, O’Rourke KP, et al.Inducible in vivo genome editing with CRISPR-Cas9. Nat Biotechnol. 2015;33:390–394.
    60. Quadros RM, Miura H, Harms DW, et al.Easi-CRISPR: a robust method for one-step generation of mice carrying conditional and insertion alleles using long ssDNA donors and CRISPR ribonucleoproteins. Genome Biol. 2017;18:92.
    61. Yu C, Liu Y, Ma T, et al.Small molecules enhance CRISPR genome editing in pluripotent stem cells. Cell Stem Cell. 2015;16:142–147.
    62. Zetsche B, Gootenberg JS, Abudayyeh OO, et al.Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell. 2015;163:759–771.
    63. Perez-Pinera P, Kocak DD, Vockley CM, et al.RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nat Methods. 2013;10:973–976.
    64. Thakore PI, D’Ippolito AM, Song L, et al.Highly specific epigenome editing by CRISPR-Cas9 repressors for silencing of distal regulatory elements. Nat Methods. 2015;12:1143–1149.
    65. Chen B, Gilbert LA, Cimini BA, et al.Dynamic imaging of genomic loci in living human cells by an optimized CRISPR/Cas system. Cell. 2013;155:1479–1491.
    66. Xiong T, Meister GE, Workman RE, et al.Targeted DNA methylation in human cells using engineered dCas9-methyltransferases. Sci Rep. 2017;7:6732.
    67. Komor AC, Kim YB, Packer MS, Zuris JA, Liu DRProgrammable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature. 2016;533:420–424.
    68. Gaudelli NM, Komor AC, Rees HA, et al.Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage. Nature. 2017;551:464–471.
    69. Zuo E, Sun Y, Wei W, et al.Cytosine base editor generates substantial off-target single-nucleotide variants in mouse embryos. Science. 2019;364:289–292.
    70. Yang X, Jounaidi Y, Mukherjee K, et al.Drug-selective anesthetic insensitivity of zebrafish lacking γ-aminobutyric acid type a receptor β3 subunits. Anesthesiology. 2019;131:1276–1291.
    71. Heidenreich M, Zhang FApplications of CRISPR-Cas systems in neuroscience. Nat Rev Neurosci. 2016;17:36–44.
    72. Thomson JA, Itskovitz-Eldor J, Shapiro SS, et al.Embryonic stem cell lines derived from human blastocysts. Science. 1998;282:1145–1147.
    73. Evans MJ, Kaufman MHEstablishment in culture of pluripotential cells from mouse embryos. Nature. 1981;292:154–156.
    74. Martin GRIsolation of a pluripotent cell line from early mouse embryos cultured in medium conditioned by teratocarcinoma stem cells. Proc Natl Acad Sci USA. 1981;78:7634–7638.
    75. Takahashi K, Yamanaka SInduction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell. 2006;126:663–676.
    76. Maherali N, Ahfeldt T, Rigamonti A, Utikal J, Cowan C, Hochedlinger KA high-efficiency system for the generation and study of human induced pluripotent stem cells. Cell Stem Cell. 2008;3:340–345.
    77. Stadtfeld M, Brennand K, Hochedlinger KReprogramming of pancreatic beta cells into induced pluripotent stem cells. Curr Biol. 2008;18:890–894.
    78. Eminli S, Utikal J, Arnold K, Jaenisch R, Hochedlinger KReprogramming of neural progenitor cells into induced pluripotent stem cells in the absence of exogenous Sox2 expression. Stem Cells. 2008;26:2467–2474.
    79. Aoi T, Yae K, Nakagawa M, et al.Generation of pluripotent stem cells from adult mouse liver and stomach cells. Science. 2008;321:699–702.
    80. Hanna J, Markoulaki S, Schorderet P, et al.Direct reprogramming of terminally differentiated mature B lymphocytes to pluripotency. Cell. 2008;133:250–264.
    81. Utikal J, Maherali N, Kulalert W, Hochedlinger KSox2 is dispensable for the reprogramming of melanocytes and melanoma cells into induced pluripotent stem cells. J Cell Sci. 2009;122:3502–3510.
    82. Narsinh KH, Plews J, Wu JCComparison of human induced pluripotent and embryonic stem cells: fraternal or identical twins? Mol Ther. 2011;19:635–638.
    83. Edmondson R, Broglie JJ, Adcock AF, Yang LThree-dimensional cell culture systems and their applications in drug discovery and cell-based biosensors. Assay Drug Dev Technol. 2014;12:207–218.
    84. Ravi M, Paramesh V, Kaviya SR, Anuradha E, Solomon FD3D cell culture systems: advantages and applications. J Cell Physiol. 2015;230:16–26.
    85. Blazeski A, Kostecki GM, Tung LEngineered heart slices for electrophysiological and contractile studies. Biomaterials. 2015;55:119–128.
    86. Shimizu T, Yamato M, Kikuchi A, Okano TCell sheet engineering for myocardial tissue reconstruction. Biomaterials. 2003;24:2309–2316.
    87. Shkumatov A, Baek K, Kong HMatrix rigidity-modulated cardiovascular organoid formation from embryoid bodies. PLoS One. 2014;9:e94764.
    88. Broutier L, Mastrogiovanni G, Verstegen MM, et al.Human primary liver cancer-derived organoid cultures for disease modeling and drug screening. Nat Med. 2017;23:1424–1435.
    89. Luca AC, Mersch S, Deenen R, et al.Impact of the 3D microenvironment on phenotype, gene expression, and EGFR inhibition of colorectal cancer cell lines. PLoS One. 2013;8:e59689.
    90. Mannhardt I, Breckwoldt K, Letuffe-Brenière D, et al.Human engineered heart tissue: analysis of contractile force. Stem Cell Reports. 2016;7:29–42.
    91. Zimmermann WH, Melnychenko I, Eschenhagen TEngineered heart tissue for regeneration of diseased hearts. Biomaterials. 2004;25:1639–1647.
    92. Eschenhagen T, Eder A, Vollert I, Hansen APhysiological aspects of cardiac tissue engineering. Am J Physiol Heart Circ Physiol. 2012;303:H133–H143.
    93. Mannhardt I, Warncke C, Trieu HK, Müller J, Eschenhagen TPiezo-bending actuators for isometric or auxotonic contraction analysis of engineered heart tissue. J Tissue Eng Regen Med. 2019;13:3–11.
    94. Li RA, Keung W, Cashman TJ, et al.Bioengineering an electro-mechanically functional miniature ventricular heart chamber from human pluripotent stem cells. Biomaterials. 2018;163:116–127.
    95. Kadoshima T, Sakaguchi H, Nakano T, et al.Self-organization of axial polarity, inside-out layer pattern, and species-specific progenitor dynamics in human ES cell-derived neocortex. Proc Natl Acad Sci USA. 2013;110:20284–20289.
    96. Mariani J, Simonini MV, Palejev D, et al.Modeling human cortical development in vitro using induced pluripotent stem cells. Proc Natl Acad Sci USA. 2012;109:12770–12775.
    97. Bagley JA, Reumann D, Bian S, Lévi-Strauss J, Knoblich JAFused cerebral organoids model interactions between brain regions. Nat Methods. 2017;14:743–751.
    98. Bershteyn M, Nowakowski TJ, Pollen AA, et al.Human iPSC-derived cerebral organoids model cellular features of lissencephaly and reveal prolonged mitosis of outer radial glia. Cell Stem Cell. 2017;20:435.e4–449.e4.
    99. Nzou G, Wicks RT, Wicks EE, et al.Human cortex spheroid with a functional blood brain barrier for high-throughput neurotoxicity screening and disease modeling. Sci Rep. 2018;8:7413.
    100. Saikia M, Burnham P, Keshavjee SH, et al.Simultaneous multiplexed amplicon sequencing and transcriptome profiling in single cells. Nat Methods. 2019;16:59–62.
    101. van der Helm MW, van der Meer AD, Eijkel JC, van den Berg A, Segerink LIMicrofluidic organ-on-chip technology for blood-brain barrier research. Tissue Barriers. 2016;4:e1142493.
    102. Park TE, Mustafaoglu N, Herland A, et al.Hypoxia-enhanced Blood-Brain Barrier Chip recapitulates human barrier function and shuttling of drugs and antibodies. Nat Commun. 2019;10:2621.
    103. Sances S, Ho R, Vatine G, et al.Human iPSC-derived endothelial cells and microengineered organ-chip enhance neuronal development. Stem Cell Reports. 2018;10:1222–1236.
    104. Vatine GD, Barrile R, Workman MJ, et al.Human iPSC-derived blood-brain barrier chips enable disease modeling and personalized medicine applications. Cell Stem Cell. 2019;24:995.e6–1005.e6.
    105. Kimura H, Sakai Y, Fujii TOrgan/body-on-a-chip based on microfluidic technology for drug discovery. Drug Metab Pharmacokinet. 2018;33:43–48.
    106. Sun N, Longaker MT, Wu JCHuman iPS cell-based therapy: considerations before clinical applications. Cell Cycle. 2010;9:880–885.
    107. Burridge PW, Holmström A, Wu JCChemically defined culture and cardiomyocyte differentiation of human pluripotent stem cells. Curr Protoc Hum Genet. 2015;87:21.3.1–21.3.15.
    108. Ebert A, Joshi AU, Andorf S, et al.Proteasome-dependent regulation of distinct metabolic states during long-term culture of human iPSC-derived cardiomyocytes. Circ Res. 2019;125:90–103.
    109. Li Z, Wang W, Kratz K, et al.Influence of surface roughness on neural differentiation of human induced pluripotent stem cells. Clin Hemorheol Microcirc. 2016;64:355–366.
    110. Efthymiou A, Shaltouki A, Steiner JP, et al.Functional screening assays with neurons generated from pluripotent stem cell-derived neural stem cells. J Biomol Screen. 2014;19:32–43.
    111. Pei Y, Peng J, Behl M, et al.Comparative neurotoxicity screening in human iPSC-derived neural stem cells, neurons and astrocytes. Brain Res. 2016;1638:57–73.
    112. Ehrlich M, Mozafari S, Glatza M, et al.Rapid and efficient generation of oligodendrocytes from human induced pluripotent stem cells using transcription factors. Proc Natl Acad Sci USA. 2017;114:E2243–E2252.
    113. Topol A, Tran NN, Brennand KJA guide to generating and using hiPSC derived NPCs for the study of neurological diseases. J Vis Exp. 2015;96:e52495.
    114. Zhang Y, Pak C, Han Y, et al.Rapid single-step induction of functional neurons from human pluripotent stem cells. Neuron. 2013;78:785–798.
    115. Garg P, Garg V, Shrestha R, Sanguinetti MC, Kamp TJ, Wu JCHuman induced pluripotent stem cell-derived cardiomyocytes as models for cardiac channelopathies: a primer for non-electrophysiologists. Circ Res. 2018;123:224–243.
    116. Soldner F, Jaenisch RMedicine. iPSC disease modeling. Science. 2012;338:1155–1156.
    117. Shinnawi R, Huber I, Maizels L, et al.Monitoring human-induced pluripotent stem cell-derived cardiomyocytes with genetically encoded calcium and voltage fluorescent reporters. Stem Cell Reports. 2015;5:582–596.
    118. Leyton-Mange JS, Mills RW, Macri VS, et al.Rapid cellular phenotyping of human pluripotent stem cell-derived cardiomyocytes using a genetically encoded fluorescent voltage sensor. Stem Cell Reports. 2014;2:163–170.
    119. Lin Y, Liu H, Klein M, et al.Efficient differentiation of cardiomyocytes and generation of calcium-sensor reporter lines from nonhuman primate iPSCs. Sci Rep. 2018;8:5907.
    120. Lin MZ, Schnitzer MJGenetically encoded indicators of neuronal activity. Nat Neurosci. 2016;19:1142–1153.
    121. Chang ACY, Chang ACH, Nicin LAn in vitro model for identifying cardiac side effects of anesthetics. Anesth Analg. 2020;130:e1–e4.
    122. Shi Y, Kirwan P, Smith J, Robinson HP, Livesey FJHuman cerebral cortex development from pluripotent stem cells to functional excitatory synapses. Nat Neurosci. 2012;15:477, S1–486, S1.
    123. Cadwell CR, Scala F, Li S, et al.Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seq. Nat Protoc. 2017;12:2531–2553.
    124. Fuzik J, Zeisel A, Máté Z, et al.Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nat Biotechnol. 2016;34:175–183.
    125. Hemphill S, McMenamin L, Bellamy MC, Hopkins PMPropofol infusion syndrome: a structured literature review and analysis of published case reports. Br J Anaesth. 2019;122:448–459.
    126. Kido K, Ito H, Yamamoto Y, Makita K, Uchida TCytotoxicity of propofol in human induced pluripotent stem cell-derived cardiomyocytes. J Anesth. 2018;32:120–131.
    127. Zhao X, Yang Z, Liang G, et al.Dual effects of isoflurane on proliferation, differentiation, and survival in human neuroprogenitor cells. Anesthesiology. 2013;118:537–549.
    128. Collo G, Cavalleri L, Chiamulera C, Merlo Pich EKetamine increases the expression of GluR1 and GluR2 α-amino-3-hydroxy-5-methy-4-isoxazole propionate receptor subunits in human dopaminergic neurons differentiated from induced pluripotent stem cells. Neuroreport. 2019;30:207–212.
    129. Shi Y, Inoue H, Wu JC, Yamanaka SInduced pluripotent stem cell technology: a decade of progress. Nat Rev Drug Discov. 2017;16:115–130.
    130. Ebert AD, Liang P, Wu JCInduced pluripotent stem cells as a disease modeling and drug screening platform. J Cardiovasc Pharmacol. 2012;60:408–416.
    131. Farkhondeh A, Li R, Gorshkov K, et al.Induced pluripotent stem cells for neural drug discovery. Drug Discov Today. 2019;24:992–999.
    132. Chang CJ, Kotini AG, Olszewska M, et al.Dissecting the contributions of cooperating gene mutations to cancer phenotypes and drug responses with patient-derived iPSCs. Stem Cell Reports. 2018;10:1610–1624.
    Copyright © 2020 International Anesthesia Research Society