Abstract

Unmarried-cell omics sequencing was start accomplished for the transcriptome in 2009, which was followed by fast development of technologies for profiling the genome, DNA methylome, 3D genome architecture, chromatin accessibility, histone modifications, etc., in an individual cell. In this review we mainly focus on the contempo progress in iv topics in the unmarried-cell omics field: single-cell epigenome sequencing, unmarried-cell genome sequencing for lineage tracing, spatially resolved single-cell transcriptomics and tertiary-generation sequencing platform-based single-prison cell omics sequencing. We also discuss the potential applications and future directions of these single-cell omics sequencing technologies for dissimilar biomedical systems, specially for the human being stem cell field.

Introduction

A single jail cell zygote at the starting bespeak of our life develops into 37 trillion cells in our body. The huge cellular heterogeneity and complexity represent a major bottleneck and pose bully challenges for research on human development, stem cell biology and many other fields. In many organs of an adult individual, stem cells are continuously self-renewing and differentiating to maintain the lifelong physiological functions of the organs. Stalk cells are mostly rare in organs and are located at specific positions surrounded and controlled by well-organized niche cells. They are quite often heterogeneous, containing distinct subtypes or distinct biological states, such as quiescent and actively proliferating states, and there are also intermediate prison cell subpopulations during their multi-lineage differentiation processes. In recent years, single-cell omics sequencing technologies take been resolving many of these issues and revolutionizing the stem cell field. This starts from the commencement single cell RNA-seq (scRNA-seq) technique developed in 2009
i
. With rapid development of the engineering science, including tremendous comeback of throughput, accurateness, automation, and commercialization, scRNA-seq techniques have been widely applied to accost disquisitional biological and medical questions. In this review, we volition discuss recent advances in single-cell omics sequencing technologies in four topics. Showtime, epigenetic regulation stands at the center of the gene regulatory networks, study of which provides of import insights into how transcription is regulated and how epigenetic memory is established and maintained in stem cells as well every bit their differentiated progenies. The epigenome of a jail cell is all of the epigenetic information stored and maintained in a cell. Information technology is comprised of a variety of precisely regulated and tightly interconnected epigenetic features including chromatin states, 3D genome architecture, DNA methylation, histone modifications, as well as the specific binding of transcription factors or non-coding RNAs onto the chromatin. Unmarried-cell epigenome sequencing techniques for many of these features accept been established and accept been routinely used for stem cell biology studies. Second, some other layer of information crucial for understanding stalk cell biology is the trajectory or lineage ‘history’ of a jail cell during stem prison cell self-renewal and differentiation. This information is vital to rigorously ostend the multi-lineage differentiation potentials of stem cells
in vivo, which is one of the defining features of stem cells. Since genetic manipulation is in general prohibited for humans, endogenous genetic variants in the genome of a cell in human bodies, including single-nucleotide variants (SNVs), copy number variations (CNVs), Indels, structure variations (SVs), variations of microsatellites or other repetitive elements, as well as mitochondrial DNA mutations, provide invaluable data for tracing the developmental trajectory of stem cells in intact man tissues
in vivo. The rapidly improving single-jail cell genome sequencing technologies are satisfying such lineage-tracing requirements for human stem cell studies. 3rd, stalk cells and their descendants, equally well as the surrounding niche cells, are usually well spatially organized in the tissues. And then the spatial organisation and interaction information is important for agreement stem jail cell biology
in vivo. This outcome can be addressed by spatially resolved single-cell transcriptome sequencing techniques. Quaternary, third-generation sequencing (TGS) technologies have progressed chop-chop in recent years. Combining TGS and single-cell omics technologies provides novel data for culling splicing and other crucial biological features of an individual prison cell.

Unmarried-jail cell epigenome sequencing

Single-prison cell epigenome sequencing engineering science is particularly challenging since the epigenetic information is scattered in the genome of a diploid jail cell, which has only two copies of the genomic DNA. As a comparison, single-prison cell transcriptome analysis is somehow easier with many expressed genes in a diploid prison cell having over a dozen copies of the mRNAs. A highly sensitive enzyme or chemical reaction is essential for single-cell epigenome sequencing technology. To make the loss of DNA as minimal every bit possible, the experimental steps should be uncomplicated. Specially if a barcode tin can be linked to a prison cell at an early pace, hundreds to thousands of individual cells can be pooled together for subsequent operations, which volition greatly increment the throughput of the method. Droplet- and microchip-based methods greatly facilitate automation and a combinatorial indexing strategy can increase the throughput in an exponential way. It is noteworthy that Tn5 transposase shows excellent performance in sensitivity, simplicity, early barcoding and throughput, and thus has stood at the center of technological improvement of unmarried-jail cell epigenome sequencing in contempo years. Bioinformatics tools take too been adult to tackle the challenges of the thin nature of single-prison cell epigenomic data. For example, chromVAR measures motifs or functional annotations together instead of individual open up chromatin regions, while ArchR uses an iterative dimensionality reduction approach
2
,

3
.

Chromatin states

Chromatin states stand for the agile or repressive condition of the regulatory genomic regions in a prison cell. Practically, the agile chromatin states tin can be assessed by accessibility of an enzyme such equally transposase, DNase I, micrococcal nuclease (MNase), or GpC methyltransferase, by which single-jail cell chromatin accessibility sequencing techniques have been established

4–9
. Among these strategies, the Tn5 transposon-based ATAC-seq (Analysis for Targeting Accessible-Chromatin with high-throughout sequencing) method simultaneously inserts, fragments and adds adaptor tags to the active chromatin regions in a jail cell and thus is excellent for low-cost and high-throughput analysis

10
. Using plate-, droplet-, or combinatorial indexing-based methods, recent studies have shown that thousands to hundreds of thousands of individual cells can be analyzed in a single sample

11–xv
. A major shortcoming of ATAC-seq is that it cannot directly discover the repressive chromatin states that can be reliably detected by the GpC methyltransferases-based methods. The latter tin can also simultaneously analyze both chromatin states and endogenous Deoxyribonucleic acid methylation of a cell, and take relatively college resolution, equally a GpC dinucleotide (GCH) occurs approximately every 25 bp in the man genome. Both the GpC methyltransferases- and transposon-based methods have also been shown to be able to simultaneously analyze CNVs at megabase resolution
sixteen
,

14
.

3D genome architecture

While the chromatin state provides information on where the chromatin opens, 3D genome architecture analysis provides information on how a genome is spatially and structurally organized and compartmentalized, as well every bit how different genomic regions collaborate with each other in a cell. Since the first single-cell Hi-C technique was established, which detects ∼1 000 contacts in an private cell, the methodology has been continuously improved, and the latest techniques are able to notice >1 1000000 contacts in an individual diploid cell

17–xix
. The technological improvement includes omitting the biotin enrichment step, usage of highly efficient single-cell whole genome amplification methods such as multiplex finish-tagging amplification (META) and multiple displacement amplification (MDA), as well every bit higher sequencing depth. This leads to a resolution of ∼20 kb for analyzing the 3D structures in an private cell, which is capable of distinguishing ii parental genomes of a cell and different neuronal subtypes
xx
,

19
. On the other hand, the throughput of the unmarried-prison cell Hi-C technique has been increased past using combinatorial indexing and Tn5 transposase/plate-based strategies
21
,

22
.

Histone modifications and transcription factor bindings

Histone modifications contribute greatly to the organization of chromatin structures and regulation of gene transcription. Chromatin immunoprecipitation (ChIP) is a widely used method for detecting modifications of histones in nucleosomes of chromatin. Realizing that low sensitivity and specificity of antibody capture is the principal obstacle in single-cell ChIP-seq analysis, cell-specific barcodes are added before aggregating the cells for immunoprecipitation

23–25
. Among these methods, Drop-ChIP and scChIP-seq add jail cell barcodes by MNase digestion and ligation with a droplet microfluidics workflow, while itChIP adds jail cell barcodes by Tn5 transposase tagmentation with a chromatin opening step. These methods are able to detect ∼1 000 to ∼10 000 unique reads per cell.

Enzyme-tethering represents a non-immunoprecipitation chromatin profiling approach that is becoming increasingly popular and has been adapted to single-cell analysis

26–31
. In these techniques, Tn5 transposase, MNase, or adenine methyltransferase is tethered to protein A that binds to the antibody, directly to the antibody, or directly to the target chromatin protein, which allows marking of the genomic regions with specific histone marks. ChIC, CUN&RUN, and scChIC use MNase, while scCUT&Tag, COBATCH, Deed-seq, and ChIL–seq use Tn5 transposase. A cardinal cation-activation stride, Catwo+
for MNase and Mg2+
for Tn5 transposase, allows activation of the enzyme activity in a short time window after washing off the nonspecifically-leap enzyme and drastically increases the signal-to-noise ratio. Every bit the Tn5-based method simultaneously adds tagged sequences, it is more convenient for high-throughput single-cell epigenomics analysis. The current enzyme-tethering unmarried-cell techniques are able to discover several thousand unique reads per individual prison cell.

DNA methylation

DNA methylation comprises another critical epigenetic layer showing jail cell-blazon-specific patterns. Unmarried-jail cell DNA methylome sequencing techniques have been established using diverse strategies including the reduced representation bisulfite sequencing (RRBS)- and the post-bisulfite adaptor tagging (PBAT)-based methods

32–34
. The mapping efficiency and throughput of PBAT-based methods tin can be increased past using three′ tagging techniques such as adaptase and TdT tailing; nevertheless since only i round of random amplification is used, the coverage is decreased
35
,

36
. A sci-MET method has applied a combinatorial indexing strategy for increasing the throughput, with the showtime and second rounds of barcodes being incorporated by Tn5 transposon and random priming, respectively
37
. Conventional RRBS enriches CpG-containing regions by selecting genomic regions between a pair of MspI (CCGG) sites, but as well covers the whole genome with many randomly fragmented genomic fragments. Recently, a single-cell extended-representation bisulfite sequencing (scXRBS) method uses an alternative approach by ligating the adaptor to a single MspI site, and thus achieves a new balance between coverage and enrichment of functionally relevant genomic regions
38
.

Joint analysis of chromatin states and transcriptome

Methods for joint assay of multiple omics in the aforementioned individual cell accept been achieved by physical separation of different omics molecules, parallel indexing, or parallel capturing
39
. In recent years, several methods have been reported for jointly detecting chromatin accessibility and transcriptome in an individual prison cell, which use various strategies for barcoding and separating ii types of information

40–44
. sci-Machine uses well-specific barcodes to perform
in situ
reverse transcription and Tn5 transposition separately
40
. The scCAT-seq method physically separates the nucleus and cytoplasmic RNA by centrifugation
42
. Both Paired-seq and SHARE-seq use a combinatorial indexing strategy, in which two to three rounds of barcoding are performed, adding barcodes to the 5′ ends of both the Tn5 and RT primers by split-and-pool. To divide the chromatin accessibility and transcriptome libraries, Paired-seq uses a restriction enzyme strategy, while SHARE-seq uses a biotin affinity pull-down strategy
43
,

44
. SNARE-seq is a droplet-based method; it uses a barcoded oligo(dT)-bearing splint oligonucleotide for simultaneously performing a reverse transcription reaction for capturing the transcriptome and a ligation reaction for capturing the chromatin accessibility information
41
.

Single-cell lineage tracing

Unmarried-cell lineage tracing techniques have recently been developed past using a combination of transposons or CRISPR/Cas9 genome editing and single-prison cell transcriptome sequencing
45
. Even so, these genetic manipulation-based methods are not suitable for
in vivo
study of humans. Genome sequence information is continuously changing during development of the homo from zygote to adult and further ageing processes due to stochastic genetic mutations. So the genetic mutations are intrinsic and platonic ‘markers’ for lineage tracing of a cell in the human body. In fact, they have been widely used for lineage tracing of tumor cells at bulk levels for many years and are the basis for tumorigenesis studies. Contempo studies accept reported the utilise of these endogenous changes of genome and mitochondrial Deoxyribonucleic acid (mtDNA) information for lineage tracing of homo stem cells in development and ageing, using clones derived from single cells or single-cell genome sequencing technologies.

Single-jail cell genome sequencing

Single jail cell whole genome amplification (scWGA) techniques such equally degenerated oligonucleotide primer (DOP)-PCR
46

and MDA
47

have long been reported. More recently, several new methods, including MALBAC, eMDA, LIANTI, SISSOR, and META-CS, have been developed

48–52
. The general characters of these methods are shown in Table1. MDA uses Phi29 for isothermal single-prison cell whole genome amplification
47
. Every bit Phi29 is of high fidelity with about i nucleotide per 108
error rate, MDA has relatively high accurateness for calling SNVs, and has been applied to unmarried-cell genome lineage tracing
53
. Ane disadvantage of MDA is its exponential-like fashion of distension of genomic DNA, which results in amplification of initial extension errors and dampens coverage uniformity. LIANTI uses T4 RNA polymerase to linearly amplify the original template hundreds of times, which increases uniformity and accurateness
48
. Further, META-CS and SISSOR learn data from both strands (Watson and Crick strands, or duplex) for reciprocal corrections, which gives even college accuracy for calling SNVs
49
,

51
. The duplex data tin besides be recalled in MDA by using single-nucleotide polymorphism (SNP) information, though the number of informative SNVs is reduced

54
. Further investigations are needed to reach the extremely challenging requirement of single-prison cell genome sequencing-based lineage tracing.

Table 1.

Unmarried-cell genome sequencing techniques.

Uniformity Accuracy Coverage Operation Reference
DOP-PCR +++ + + +++ 46
MDA ++ +++ +++ +++ 47
eMDA +++ +++ +++ ++ 50
SISSOR +++ ++++ ++ ++ 49
MALBAC +++ ++ ++ +++ 52
LIANTI ++++ +++ ++++ ++ 48
META-CS +++ ++++ +++ +++ 51
Uniformity Accurateness Coverage Operation Reference
DOP-PCR +++ + + +++ 46
MDA ++ +++ +++ +++ 47
eMDA +++ +++ +++ ++ l
SISSOR +++ ++++ ++ ++ 49
MALBAC +++ ++ ++ +++ 52
LIANTI ++++ +++ ++++ ++ 48
META-CS +++ ++++ +++ +++ 51

Tabular array 1.

Single-cell genome sequencing techniques.

Uniformity Accuracy Coverage Functioning Reference
DOP-PCR +++ + + +++ 46
MDA ++ +++ +++ +++ 47
eMDA +++ +++ +++ ++ fifty
SISSOR +++ ++++ ++ ++ 49
MALBAC +++ ++ ++ +++ 52
LIANTI ++++ +++ ++++ ++ 48
META-CS +++ ++++ +++ +++ 51
Uniformity Accuracy Coverage Performance Reference
DOP-PCR +++ + + +++ 46
MDA ++ +++ +++ +++ 47
eMDA +++ +++ +++ ++ 50
SISSOR +++ ++++ ++ ++ 49
MALBAC +++ ++ ++ +++ 52
LIANTI ++++ +++ ++++ ++ 48
META-CS +++ ++++ +++ +++ 51

Uniformity is measured by the coefficient of variation of the sequence-dependent bias along the genome. At a bin size of ane 000 kb, LIANTI shows the highest uniformity with a value of ∼0.03, while eMDA, MALBAC (normalized), META-CS, and DOP-PCR show a range between 0.1 and 0.15, and MDA shows a value >0.21; the value of SISSOR has not been described and is expected to exist similar to eMDA.

Accuracy is measured by the false positive rate (FPR). META-CS and SISSOR, which give strand-specific information, have the highest accuracy with the lowest FPR (<2.4
|$\times$|
10–8). LIANTI measures the linear amplification product many times and has the second highest accuracy with a FPR of 5.four
|$\times$|
ten–6. MDA and eMDA rank third with a FPR of 1.3
|$\times$|
10–4. MALBAC and DOP-PCR show FPRs of 3.viii
|$\times$|
x–4
and 9.6
|$\times$|
10–iv, respectively.

Coverage of LIANTI is the highest, covering 95% of the genome of a human diploid cell past sequencing 83 Gb information, while META-CS (64% by 18 Gb data), MDA (87% past 85 Gb data), and eMDA (72% by 30 Gb data) rank 2nd, followed by MALBAC (Yikon, 73% by 94 Gb data), SISSOR (64% by 195 Gb data), and DOP-PCR (45% by 84 Gb information).

Experimental operations of DOP-PCR, MDA, MALBAC, and META-CS are easier than those of eMDA, SISSOR, and LIANTI.

Genome sequence information in a cell for lineage tracing

Different types of genetic variants accept unlike characters for the purpose of lineage tracing. Nucleotide substitution changes are estimated to occur at a frequency of ∼ane per cell sectionalization in a homo cell. As expected, the number of SNVs increases with accumulating cell divisions during development and ageing. A fetal hematopoietic stem cell has tens of somatic SNVs while a hematopoietic stem cell from a middle-aged adult has ∼1 000 somatic SNVs
55
,

56
. Similarly, adult stalk cells of colon, modest intestine, and liver have a few thousands somatic SNVs, with an accumulation of ∼36 mutations per twelvemonth
57
. Thus SNVs represent a rich source of endogenous genetic polymorphisms for lineage tracing.

Microsatellites are expected to be an fifty-fifty richer source of endogenous polymorphism every bit the mutation rate of a microsatellite is as loftier as 10–3
to 10–five/locus/cell division. It is estimated that 50 microsatellite mutations occur per prison cell partition in humans and the complete cell lineage tree can be reconstructed using this data
58
. Obstacles for full application of this information include fault-prone sequencing, difficulty in capturing, and short read length.

The repetitive elements including long interspersed echo elements (LINEs), short interspersed nuclear elements (SINEs), and long last repeats (LTRs) contain a large portion of the man genome. A number of LINEs such as LINE1 and SINEs such as Alus are still agile in humans, and transposition of LINE1 and Alus occurs at a rate of about 1 per 140 generation and 15 per generation, respectively
59
. Depression-rate somatic mutations of LINE1 take been reported in individual neurons at rates ranging from <0.1 to >ten insertions per cell
threescore
,

61
. Also, insertions accept been detected in normal gastrointestinal tissues and occur very early during the development of gastrointestinal tumors
62
. Though the frequency is low, the unique insertion sequences of new transposon elements can be definitely verified and detected, which facilitates using them as skilful markers for lineage tracing
63
.

Information technology is well known that CNVs occur in high frequency during human early development, although well-nigh of them lead to death of the embryo or are self-corrected by elimination of the aneuploid cells in chimeric embryos; how frequently the early on CNVs result in human chimeras is non known. In contempo years, somatic chimeric CNVs have been identified in many cell types including neurons, blood cells, and fibroblasts

64–69
. The reward of CNVs equally an endogenous mark for lineage tracing is that they can be detected by relatively low-depth single-jail cell genome sequencing. However, the disadvantages include low frequency and difficulty of verification.

mtDNA, which is a ∼16 kb long round genome, is likewise a part of the cellular genome in addition to the nuclear genome. mtDNA mutation occurs 10–100 times more than genomic mutation, and in that location are hundreds of copies of mtDNAs in an private cell. Also, mtDNA tin be detected in other unmarried-cell omics data, including scATAC-seq, scRNA-seq, and single-jail cell genome sequencing. These features make mtDNA a unique mark for simultaneous lineage tracing and jail cell-state detection
70
.

Several contempo studies have reported lineage tracing using single cells, clones derived from single cells, or microdissection

71–73
,

63
,

55
,

53
,

74
,

56
. Lee-Six
et al. analyzed 140 colonies derived from unmarried hematopoietic stalk cells of a 59-year-erstwhile male individual and identified an average of 1 023 SNVs and 20 pocket-sized insertion/deletions in each clone. They constructed a phylogenetic tree that revealed clonal relationships among these 140 colonies. The results showed a rapid population expansion of hematopoietic stem cells during early life and estimated that there are between fifty 000 and 200 000 hematopoietic stem cells actively making white blood cells at whatever specific time
55
. In another study, Spencer Chapman
et al. analyzed 511 colonies derived from haematopoietic stalk cells of two human fetuses and identified 25.5 and 41.ix SNVs per clone in the 8 and eighteen post-conception week fetuses, respectively
56
. These SNVs allowed for reconstructing phylogenetic trees for early on human embryonic development, which revealed several interesting findings, including an unequal contribution of each of the two-cell stage blastomeres to the blood compartment, a college mutation rate in the first three cell divisions, and hypoblast origin of the actress-embryonic mesoderm and primitive blood.

Cells like neurons are mail service-mitotic and not able to proliferate, limiting the use of clone distension and bulk sequencing strategies. Evrony
et al. analyzed xvi cognitive cortex neurons by MDA and whole genome sequencing, which immune for identification of two new L1 retrotranspositions and one poly-A microsatellite mutation. They designed a custom droplet digital PCR (ddPCR) assay and analyzed these mutations in diverse brain regions, which revealed one clone limiting to the left middle frontal gyrus and another distributing over the entire left hemisphere
63
. In some other study by the same group, 36 neurons were analyzed for SNVs and custom ddPCR was used for revealing the polyclonal architecture of the human cerebral cortex
53
.

The field of lineage tracing at single-jail cell resolution is developing apace. The approach can exist used to reconstruct the developmental history of stem cells, showing their precursors and progenies. It can also be used for estimating the number of stem cells that requite rising to the differentiated cells, and whether or not they give equivalent contributions to these progeny cells. For example, Spencer Chapman
et al. showed that two blastomeres in a ii-prison cell stage embryo contribute unequally to the body
56
. Likewise, during tissue injury, it is important to know which types of cells contribute to repair the injured tissue and recover its physiological functions, every bit has been studied in mice past genetic lineage tracing. Single-jail cell multiple-omics sequencing for genome and epigenome or transcriptome may assist in elucidating the situation in humans.

Single-prison cell spatial transcriptome

Spatial localization is essential for determining cellular fate. Single cell spatially resolved transcriptome technologies accept been developed and improved rapidly in recent years, and are particularly useful for homo study for which genetic labeling techniques are non applicative
75
. The methods of spatially resolved transcriptomics are listed in Table2. Amidst them, two types of technologies, unmarried-molecule FISH (smFISH)-based ones and
in situ
sequencing-based ones, provide single-jail cell resolution. The first one, including seqFISH and MERFISH, uses combinatorial barcodes for smFISH
76
,

77
. seqFISH labels each RNA past a combinatorial gear up of colored probes, through multiple rounds of sequential hybridizations and clearance of the probes
77
. MERFISH uses a like combinatorial labeling strategy with the use of readout probes instead of straight labeling probes
76
. The latest versions of both methods are able to epitome mRNAs for upwards to ten 000 genes in a single cell
78
,

79
.

Table two.

Spatially resolved transcriptome techniques.

Full name Strategy Targeting Single-cell resolution References
seqFISH Sequential fluorescence
in situ
hybridization (FISH)
Combinatorial barcodes for single-molecule FISH Aye Yes 78,77
MERFISH Multiplexed error-robust (FISH) Combinatorial barcodes for unmarried-molecule FISH Yes Yes 76,79
ISS In situ
sequencing
In situ
sequencing
Yes Yep 80
FISSEQ Fluorescence
in situ
sequencing
In situ
sequencing
No Yeah 81
ExSeq Expansion sequencing In situsequencing with expansion microscopy No Yes 82
STARmap Spatially-resolved transcript amplicon readout mapping In situ
sequencing
Yes Yes 83
TIVA Transcriptome
in vivo
analysis
Photoactive tag Yes Yes 84
Spatial Transcriptomics Spatial transcriptomics Gene ChIPs with immobilized reverse-transcription oligo (dT) primers No No 85
Slide-seq Slide-seq Spatially resolved DNA-barcoded beads No Almost 86,87
Stereo-seq Spatial enhanced resolution omics-sequencing Spatially resolved DNA nanoball No Yes/nearly 88
iTranscriptome In silico
spatial transcriptome
Combination of the low-input RNA sequencing with serial cryosection and laser capture microdissection No No 89
Tomo-Seq RNA tomography sequencing Combination of the depression-input RNA sequencing with serial cryosection No No ninety
Total name Strategy Targeting Single-prison cell resolution References
seqFISH Sequential fluorescence
in situ
hybridization (FISH)
Combinatorial barcodes for unmarried-molecule FISH Yes Yeah 78,77
MERFISH Multiplexed error-robust (FISH) Combinatorial barcodes for unmarried-molecule FISH Yes Yes 76,79
ISS In situ
sequencing
In situ
sequencing
Yeah Yes eighty
FISSEQ Fluorescence
in situ
sequencing
In situ
sequencing
No Yep 81
ExSeq Expansion sequencing In situsequencing with expansion microscopy No Yeah 82
STARmap Spatially-resolved transcript amplicon readout mapping In situ
sequencing
Yes Yes 83
TIVA Transcriptome
in vivo
analysis
Photoactive tag Yes Aye 84
Spatial Transcriptomics Spatial transcriptomics Gene ChIPs with immobilized reverse-transcription oligo (dT) primers No No 85
Slide-seq Slide-seq Spatially resolved DNA-barcoded beads No Well-nigh 86,87
Stereo-seq Spatial enhanced resolution omics-sequencing Spatially resolved Dna nanoball No Yep/virtually 88
iTranscriptome In silico
spatial transcriptome
Combination of the low-input RNA sequencing with serial cryosection and laser capture microdissection No No 89
Tomo-Seq RNA tomography sequencing Combination of the low-input RNA sequencing with serial cryosection No No 90

Tabular array 2.

Spatially resolved transcriptome techniques.

Full name Strategy Targeting Unmarried-prison cell resolution References
seqFISH Sequential fluorescence
in situ
hybridization (FISH)
Combinatorial barcodes for single-molecule FISH Aye Yes 78,77
MERFISH Multiplexed error-robust (FISH) Combinatorial barcodes for single-molecule FISH Yes Aye 76,79
ISS In situ
sequencing
In situ
sequencing
Yes Yes 80
FISSEQ Fluorescence
in situ
sequencing
In situ
sequencing
No Yes 81
ExSeq Expansion sequencing In situsequencing with expansion microscopy No Yes 82
STARmap Spatially-resolved transcript amplicon readout mapping In situ
sequencing
Yes Yes 83
TIVA Transcriptome
in vivo
analysis
Photoactive tag Yes Aye 84
Spatial Transcriptomics Spatial transcriptomics Gene Fries with immobilized reverse-transcription oligo (dT) primers No No 85
Slide-seq Slide-seq Spatially resolved Deoxyribonucleic acid-barcoded beads No Nearly 86,87
Stereo-seq Spatial enhanced resolution omics-sequencing Spatially resolved DNA nanoball No Yes/nearly 88
iTranscriptome In silico
spatial transcriptome
Combination of the low-input RNA sequencing with series cryosection and laser capture microdissection No No 89
Tomo-Seq RNA tomography sequencing Combination of the low-input RNA sequencing with serial cryosection No No ninety
Total name Strategy Targeting Single-cell resolution References
seqFISH Sequential fluorescence
in situ
hybridization (FISH)
Combinatorial barcodes for single-molecule FISH Yes Yep 78,77
MERFISH Multiplexed mistake-robust (FISH) Combinatorial barcodes for single-molecule FISH Yes Yes 76,79
ISS In situ
sequencing
In situ
sequencing
Yeah Yes 80
FISSEQ Fluorescence
in situ
sequencing
In situ
sequencing
No Yes 81
ExSeq Expansion sequencing In situsequencing with expansion microscopy No Yes 82
STARmap Spatially-resolved transcript amplicon readout mapping In situ
sequencing
Yes Yes 83
TIVA Transcriptome
in vivo
analysis
Photoactive tag Yes Yes 84
Spatial Transcriptomics Spatial transcriptomics Gene ChIPs with immobilized opposite-transcription oligo (dT) primers No No 85
Slide-seq Slide-seq Spatially resolved Deoxyribonucleic acid-barcoded chaplet No Nearly 86,87
Stereo-seq Spatial enhanced resolution omics-sequencing Spatially resolved Deoxyribonucleic acid nanoball No Yes/nearly 88
iTranscriptome In silico
spatial transcriptome
Combination of the low-input RNA sequencing with serial cryosection and laser capture microdissection No No 89
Tomo-Seq RNA tomography sequencing Combination of the low-input RNA sequencing with serial cryosection No No 90

The 2d set of methods is
in situ
sequencing (ISS, FISSEQ, STARmap, ExSeq)
fourscore
,

83
. All these methods use rolling cycle amplification for indicate amplification earlier
in situ
sequencing analysis. ISS and STARmap are targeting methods. ISS uses padlock probes for targeting
80
, and STARmap uses designed nucleic acids for directly targeting RNAs that featherbed the reverse transcription footstep and increases detection efficiency
83
. FISSEQ and ExSeq are untargeted methods, and ExSeq is an improved technique of FISSEQ that adapts the chemistry of expansion microscopy to allow high spatial resolution mapping of RNAs
82
,

81
.

Another set of spatial transcriptomics techniques use
in situ
capturing strategies
86
,

85
. These techniques are approaching single-cell resolution with the high density of on-slide capturing
87
,

88
.

Third-generation sequencing

Third-generation/real-time unmarried molecule sequencing (TGS) methods have been developing especially fast in contempo years. These include Nanopore sequencing (ONT) introduced by Oxford Nanopore Technologies, and unmarried-molecule real-time (SMRT) sequencing by Pacific Biosciences (PacBio)
91
. Emerging as a new field of single-cell omics sequencing, TGS has several unique advantages and applications, some of which have been achieved (Fig.one).

Figure 1.

TGS-based single cell sequencing technologies. Scarlet and green signal the aspects that take been accomplished, while the cherry indicates those where TGS-based methods have advantages over NGS-based methods. *The Nanopore-based technology may be used for directly sequencing the poly peptide in the hereafter
92
. Note the differences between NGS-based and TGS-based single-jail cell omics sequencing technologies (compare this figure to Fig.1 of Ref.
39)

First, TGS-based scRNA-seq techniques are powerful for detecting culling splicing or DNA rearrangements by directly sequencing the total-length intact cDNAs. Several studies including ours have recently adult TGS-based scRNA sequencing techniques including SCAN-seq, R2C2, ScISOr-Seq, ScNaUmi-seq, and RAGE-seq

93–96
. SCAN-seq is able to notice >8 000 genes in an individual mouse embryonic stem cell (mESC), exhibiting a similar sensitivity to the side by side-generation sequencing (NGS) platform-based scRNA-seq techniques such as SMART-seq2, and SUPer-seq. A large number of unannotated novel transcripts have been detected. Browse-seq detected six 487 unannotated transcripts corresponding to 3 834 genes in mESCs, and 27 250 unannotated transcripts corresponding to 9 338 genes in mouse preimplantation embryos
93
. ScISOr-Seq detected 18 173 known and sixteen 872 novel isoforms in mouse cerebellum
94
. In addition, RAGE-seq has shown the ability of the TGS-based approach for detecting fusion transcripts from somatic DNA rearrangements of T-cell-receptor (TCR) and B-cell-receptor (BCR) transcripts
95
.

2nd, while small variants such as SNVs and brusque indels can be accurately detected using NGS-based curt reads, larger structural variations (SVs) are more challenging to detect and characterize. TGS-based methods have been developed speedily to increment the reliability and resolution of SV detection
97
. Our group has recently developed a TGS-based unmarried-cell genome sequencing technique SMOOTH-seq
98
. For private cells, the technique gets long sequencing reads with an boilerplate length of 6 kb, and reaches 19% genome coverage by 0.4X sequencing depth. Except for insertions, deletions, duplications, and translocations, the technique also effectively detects extra-chromosomal round DNAs (ecDNA), being able to cover the full-length ones of <ten kb in a single read.

Third, unmarried molecular nanopore sequencing is able to straight detect epigenetic modifications such equally 5mC and 6mA. Combining such power with the enzyme accessibility of N6-methyladenosine (m6A) methyltransferase or GpC methyltransferase, iii groups accept recently reported TGS-based enzyme accessibility techniques for detecting chromatin country along single-molecules over a long distance (SMAC-seq, Fiber-seq, and nanoNOMe)

99–101
. Although they accept not been adapted to single-cell assay, these methods display the value of TGS for investigating coordination between the states of neighboring regulatory elements over big genomic regions, which opens a new avenue for future study.

Prospective

Single-prison cell omics sequencing engineering science has already fabricated fruitful progresses in the stem cell biology field. However, the electric current techniques are still not ideal for homoin vivo
stem cell studies. We expect that the techniques will further be developed and improved inside the adjacent few years. The electric current single-jail cell omics sequencing engineering science has both stiff (developed) and weak (developing) characteristics (Fig.2). The technology has loftier universality equally it is applicative in a wide range of biological research fields from plants to medicine. It gives high sensitivity and accuracy, but still requires improvement. The throughput, automation, and speed of the technology have increased greatly with the cost decreasing, only take not met clinical requirements. Especially for stem jail cell studies, the temporal and spatial resolution of the engineering is not satisfactory and is being improved. With its weaknesses being improved, single-cell omics sequencing applied science will exist routinely used to dissect the biology of stem cells, including (one) their cocky-renewal abilities or multiple lineage differentiation potentials under physiological conditions or under pathological conditions, (ii) their premature differentiation or delayed leaving from cocky-renewal style in diseased situations, (3) their microenvironments, (four) the consequences of their genetic perturbations, and (5) short-term responses to environmental changes or long-term maintenance of their fates.

Figure ii.

Characteristics of the current single-cell omics sequencing technology. Single-cell omics sequencing technology is shown shaped like a barrel with both long (developed) and short (developing) boards (left panel), and a radar chart shows the current technical states of nine major characteristics, with the more developed state of the character indicated by its being positioned more peripherally (right panel).

Characteristics of the current unmarried-jail cell omics sequencing technology. Unmarried-cell omics sequencing technology is shown shaped like a barrel with both long (developed) and brusque (developing) boards (left panel), and a radar nautical chart shows the current technical states of nine major characteristics, with the more adult state of the character indicated by its being positioned more than peripherally (right console).

Further, it will be ideal if genome, epigenome, and transcriptome tin be simultaneously analyzed for an private cell. The transcriptome will permit identifying and separating different types of cells. Information technology will likewise deed as a functional readout of the global transcriptional activity. And so how different layers of epigenomes regulate the organisation of the genome and transcriptional action of every gene can be delineated. Finally, the genome information tin can also exist used to construct the lineage relationship. In addition, how genetic changes contribute to the abnormal behavior of a stem jail cell can be analyzed. If a genetic change perturbs the expression of a factor, it may change the physiological function and phenotype of a cell. In the time to come it is possible that through single-prison cell multi-omics sequencing we will identify genetic changes in the stem cells in our body and their potential connections to the phenotypic changes of a stalk cell.

Of course, single-cell omics sequencing techniques are just a series of technologies and they cannot reply every question that arises in stem cell biology. Still, by integrating properly with other sets of powerful technologies such every bit gene editing tools and organoid 3D civilization systems and biological concepts, they volition definitely accelerate the transformation of our rich and deep cognition of stem cells in brute models into more clinically relevant knowledge of stalk cells in human being.

ACKNOWLEDGEMENTS

This work was supported by the National Key Research and Development Program of Prc (Grant No. 2018YFA0107601).

Conflict of interest

None declared. In improver, every bit an Editorial Lath Member of
Precision Clinical Medicine, the respective author Fuchou Tang was blinded from reviewing and making decisions on this manuscript.

References

1

Tang

F

,

Barbacioru

C

,

Wang

Y

, et al.

mRNA-Seq whole-transcriptome analysis of a single jail cell

.

Nat Methods

.

2009

;

half dozen

:

377

82

.. doi:

.

2

Granja

JM

,

Corces

MR

,

Pierce

SE

, et al.

ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

.

Nat Genet

.

2021

;

53

:

403

11

.. doi:

.

3

Schep

AN

,

Wu

B

,

Buenrostro

JD

, et al.

chromVAR: inferring transcription-gene-associated accessibility from single-cell epigenomic data

.

Nat Methods

.

2017

;

14

:

975

8

.. doi:

.

4

Buenrostro

JD

,

Wu

B

,

Litzenburger

UM

, et al.

Single-cell chromatin accessibility reveals principles of regulatory variation

.

Nature

.

2015

;

523

:

486

90

.. doi:

.

5

Clark

SJ

,

Argelaguet

R

,

Kapourani

CA

, et al.

scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in unmarried cells

.

Nat Commun

.

2018

;

nine

:

781

. doi:

.

half-dozen

Cusanovich

DA

,

Daza

R

,

Adey

A

, et al.

Epigenetics. Multiplex single-cell profiling of chromatin accessibility past combinatorial cellular indexing

.

Scientific discipline

.

2015

;

348

:

910

4

.. doi:

.

7

Guo

F

,

Li

L

,

Li

J

, et al.

Single-jail cell multi-omics sequencing of mouse early on embryos and embryonic stem cells

.

Jail cell Res

.

2017

;

27

:

967

88

.. doi:

.

8

Jin

W

,

Tang

Q

,

Wan

M

, et al.

Genome-wide detection of DNase I hypersensitive sites in unmarried cells and FFPE tissue samples

.

Nature

.

2015

;

528

:

142

6

.. doi:

.

nine

Pott

S

.

Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in unmarried cells

.

Elife

.

2017

;

6

:

e23203

. doi:

.

ten

Buenrostro

JD

,

Giresi

PG

,

Zaba

LC

, et al.

Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, Deoxyribonucleic acid-binding proteins and nucleosome position

.

Nat Methods

.

2013

;

10

:

1213

8

.. doi:

.

11

Cusanovich

DA

,

Hill

AJ

,

Aghamirzaie

D

, et al.

A Unmarried-Cell atlas of in vivo mammalian chromatin accessibility

.

Cell

.

2018

;

174

:

1309

24

..

e18

. doi:

.

12

Lake

BB

,

Chen

S

,

Sos

BC

, et al.

Integrative single-cell analysis of transcriptional and epigenetic states in the homo developed brain

.

Nat Biotechnol

.

2018

;

36

:

seventy

lxxx

.. doi:

.

thirteen

Preissl

Due south

,

Fang

R

,

Huang

H

, et al.

Unmarried-nucleus analysis of accessible chromatin in developing mouse forebrain reveals jail cell-blazon-specific transcriptional regulation

.

Nat Neurosci

.

2018

;

21

:

432

9

.. doi:

.

fourteen

Satpathy

AT

,

Granja

JM

,

Yost

KE

, et al.

Massively parallel unmarried-cell chromatin landscapes of human allowed cell development and intratumoral T cell burnout

.

Nat Biotechnol

.

2019

;

37

:

925

36

.. doi:

.

15

Xu

West

,

Wen

Y

,

Liang

Y

, et al.

A plate-based single-jail cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility

.

Nat Protoc

.

2021

;

16

:

4084

107

.. doi:

.

16

Bian

Due south

,

Hou

Y

,

Zhou

X

, et al.

Unmarried-cell multiomics sequencing and analyses of man colorectal cancer

.

Science

.

2018

;

362

:

1060

three

.. doi:

.

17

Flyamer

IM

,

Gassler

J

,

Imakaev

M

, et al.

Unmarried-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition

.

Nature

.

2017

;

544

:

110

4

.. doi:

.

xviii

Nagano

T

,

Lubling

Y

,

Stevens

TJ

, et al.

Single-jail cell Hullo-C reveals cell-to-jail cell variability in chromosome structure

.

Nature

.

2013

;

502

:

59

64

.. doi:

.

19

Tan

L

,

Xing

D

,

Chang

CH

, et al.

3-dimensional genome structures of single diploid human cells

.

Science

.

2018

;

361

:

924

viii

.. doi:

.

twenty

Tan

L

,

Ma

West

,

Wu

H

, et al.

Changes in genome architecture and transcriptional dynamics progress independently of sensory experience during post-natal brain development

.

Cell

.

2021

;

184

:

741

58

..

e717

. doi:

.

21

Nagano

T

,

Lubling

Y

,

Várnai

C

, et al.

Cell-cycle dynamics of chromosomal organization at single-cell resolution

.

Nature

.

2017

;

547

:

61

7

.. doi:

.

22

Ramani

5

,

Deng

X

,

Qiu

R

, et al.

Massively multiplex unmarried-cell Hello-C

.

Nat Methods

.

2017

;

14

:

263

6

.. doi:

.

23

Ai

S

,

Xiong

H

,

Li

CC

, et al.

Profiling chromatin states using single-cell itChIP-seq

.

Nat Cell Biol

.

2019

;

21

:

1164

72

.. doi:

.

24

Grosselin

G

,

Durand

A

,

Marsolier

J

, et al.

High-throughput unmarried-prison cell ChIP-seq identifies heterogeneity of chromatin states in chest cancer

.

Nat Genet

.

2019

;

51

:

1060

6

.. doi:

.

25

Rotem

A

,

Ram

O

,

Shoresh

Northward

, et al.

Single-cell Fleck-seq reveals jail cell subpopulations defined by chromatin state

.

Nat Biotechnol

.

2015

;

33

:

1165

72

.. doi:

.

26

Carter

B

,

Ku

WL

,

Kang

JY

, et al.

Mapping histone modifications in depression cell number and unmarried cells using antibody-guided chromatin tagmentation (ACT-seq)

.

Nat Commun

.

2019

;

10

:

3747

. doi:

.

27

Harada

A

,

Maehara

K

,

Handa

T

, et al.

A chromatin integration labelling method enables epigenomic profiling with lower input

.

Nat Cell Biol

.

2019

;

21

:

287

96

.. doi:

.

28

Kaya-Okur

HS

,

Wu

SJ

,

Codomo

CA

, et al.

CUT&Tag for efficient epigenomic profiling of pocket-sized samples and single cells

.

Nat Commun

.

2019

;

10

:

1930

. doi:

.

29

Schmid

M

,

Durussel

T

,

Laemmli

UK

.

Chic and ChEC; genomic mapping of chromatin proteins

.

Mol Cell

.

2004

;

16

:

147

57

.. doi:

.

30

Skene

PJ

,

Henikoff

S

.

An efficient targeted nuclease strategy for loftier-resolution mapping of Deoxyribonucleic acid binding sites

.

Elife

.

2017

;

6

:

e21856

. doi:

.

31

Wang

Q

,

Xiong

H

,

Ai

Southward

, et al.

CoBATCH for High-Throughput Single-Jail cell Epigenomic Profiling

.

Mol Jail cell

.

2019

;

76

:

206

16

..

e7

. doi:

.

32

Guo

H

,

Zhu

P

,

Wu

10

, et al.

Single-jail cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing

.

Genome Res

.

2013

;

23

:

2126

35

.. doi:

.

33

Luo

C

,

Keown

CL

,

Kurihara

L

, et al.

Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex

.

Science

.

2017

;

357

:

600

iv

.. doi:

.

34

Smallwood

SA

,

Lee

HJ

,

Angermueller

C

, et al.

Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity

.

Nat Methods

.

2014

;

11

:

817

twenty

.. doi:

.

35

Gu

C

,

Liu

South

,

Wu

Q

, et al.

Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes

.

Cell Res

.

2019

;

29

:

110

23

.. doi:

.

36

Wu

P

,

Gao

Y

,

Guo

Westward

, et al.

Using local alignment to enhance unmarried-cell bisulfite sequencing information efficiency

.

Bioinformatics

.

2019

;

35

:

3273

eight

.. doi:

.

37

Mulqueen

RM

,

Pokholok

D

,

Norberg

SJ

, et al.

Highly scalable generation of Dna methylation profiles in single cells

.

Nat Biotechnol

.

2018

;

36

:

428

31

.. doi:

.

38

Shareef

SJ

,

Bevill

SM

,

Raman

AT

, et al.

Extended-representation bisulfite sequencing of factor regulatory elements in multiplexed samples and single cells

.

Nat Biotechnol

.

2021

;

39

:

1086

94

.. doi:

.

39

Wen

L

,

Tang

F

.

Single cell epigenome sequencing technologies

.

Mol Aspects Med

.

2018

;

59

:

62

9

.. doi:

.

40

Cao

J

,

Cusanovich

DA

,

Ramani

Five

, et al.

Articulation profiling of chromatin accessibility and gene expression in thousands of unmarried cells

.

Science

.

2018

;

361

:

1380

5

.. doi:

.

41

Chen

S

,

Lake

BB

,

Zhang

K

.

High-throughput sequencing of the transcriptome and chromatin accessibility in the aforementioned cell

.

Nat Biotechnol

.

2019

;

37

:

1452

7

.. doi:

.

42

Liu

L

,

Liu

C

,

Quintero

A

, et al.

Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

.

Nat Commun

.

2019

;

10

:

470

. doi:

.

43

Ma

Southward

,

Zhang

B

,

LaFave

LM

, et al.

Chromatin potential identified by shared single-cell profiling of RNA and chromatin

.

Cell

.

2020

;

183

:

1103

16

..

e20

. doi:

.

44

Zhu

C

,

Yu

1000

,

Huang

H

, et al.

An ultra high-throughput method for single-cell joint assay of open chromatin and transcriptome

.

Nat Struct Mol Biol

.

2019

;

26

:

1063

lxx

.. doi:

.

45

Wagner

DE

,

Klein

AM

.

Lineage tracing meets unmarried-jail cell omics: opportunities and challenges

.

Nat Rev Genet

.

2020

;

21

:

410

27

.. doi:

.

46

Telenius

H

,

Carter

NP

,

Bebb

CE

, et al.

Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer

.

Genomics

.

1992

;

13

:

718

25

.. doi:

.

47

Dean

FB

,

Nelson

JR

,

Giesler

TL

, et al.

Rapid amplification of plasmid and phage Dna using Phi 29 Dna polymerase and multiply-primed rolling circle amplification

.

Genome Res

.

2001

;

11

:

1095

ix

.. doi:

.

48

Chen

C

,

Xing

D

,

Tan

L

, et al.

Single-prison cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI)

.

Science

.

2017

;

356

:

189

94

.. doi:

.

49

Chu

WK

,

Edge

P

,

Lee

HS

, et al.

Ultraaccurate genome sequencing and haplotyping of single man cells

.

Proc Natl Acad Sci U Southward A

.

2017

;

114

:

12512

vii

.. doi:

.

50

Fu

Y

,

Li

C

,

Lu

S

, et al.

Compatible and authentic unmarried-cell sequencing based on emulsion whole-genome amplification

.

Proc Natl Acad Sci U South A

.

2015

;

112

:

11923

eight

.. doi:

.

51

Xing

D

,

Tan

L

,

Chang

CH

, et al.

Accurate SNV detection in single cells by transposon-based whole-genome amplification of complementary strands

.

Proc Natl Acad Sci U Due south A

.

2021

;

118

:

e2013106118

. doi:

.

52

Zong

C

,

Lu

Due south

,

Chapman

AR

, et al.

Genome-wide detection of single-nucleotide and re-create-number variations of a single human cell

.

Science

.

2012

;

338

:

1622

six

.. doi:

.

53

Lodato

MA

,

Woodworth

MB

,

Lee

S

, et al.

Somatic mutation in single human neurons tracks developmental and transcriptional history

.

Science

.

2015

;

350

:

94

8

.. doi:

.

54

Lodato

MA

,

Rodin

RE

,

Bohrson

CL

, et al.

Crumbling and neurodegeneration are associated with increased mutations in single man neurons

.

Science

.

2018

;

359

:

555

nine

.. doi:

.

55

Lee-Half dozen

H

,

Øbro

NF

,

Shepherd

MS

, et al.

Population dynamics of normal man blood inferred from somatic mutations

.

Nature

.

2018

;

561

:

473

8

.. doi:

.

56

Spencer Chapman

M

,

Ranzoni

AM

,

Myers

B

, et al.

Lineage tracing of human development through somatic mutations

.

Nature

.

2021

;

595

:

85

xc

.. doi:

57

Blokzijl

F

,

de Ligt

J

,

Jager

M

, et al.

Tissue-specific mutation accumulation in human adult stem cells during life

.

Nature

.

2016

;

538

:

260

four

.. doi:

.

58

Chapal-Ilani

N

,

Maruvka

YE

,

Spiro

A

, et al.

Comparison algorithms that reconstruct cell lineage trees utilizing information on microsatellite mutations

.

PLoS Comput Biol

.

2013

;

nine

:

e1003297

. doi:

.

59

Kazazian

HH

Jr.,

Moran

JV

.

Mobile Deoxyribonucleic acid in Health and Disease

.

N Engl J Med

.

2017

;

377

:

361

70

.. doi:

.

lx

Evrony

GD

,

Cai

Ten

,

Lee

E

, et al.

Unmarried-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain

.

Prison cell

.

2012

;

151

:

483

96

.. doi:

.

61

Upton

KR

,

Gerhardt

DJ

,

Jesuadian

JS

, et al.

Ubiquitous L1 mosaicism in hippocampal neurons

.

Jail cell

.

2015

;

161

:

228

39

.. doi:

.

62

Ewing

AD

,

Kazazian

HH Jr

.

High-throughput sequencing reveals extensive variation in human-specific L1 content in individual human genomes

.

Genome Res

.

2010

;

20

:

1262

70

.. doi:

.

63

Evrony

GD

,

Lee

East

,

Mehta

BK

, et al.

Prison cell lineage analysis in human being encephalon using endogenous retroelements

.

Neuron

.

2015

;

85

:

49

59

.. doi:

.

64

Cai

10

,

Evrony

GD

,

Lehmann

HS

, et al.

Single-cell, genome-wide sequencing identifies clonal somatic copy-number variation in the human encephalon

.

Cell Rep

.

2015

;

10

:

645

. doi:

.

65

Jacobs

KB

,

Yeager

M

,

Zhou

W

, et al.

Detectable clonal mosaicism and its relationship to aging and cancer

.

Nat Genet

.

2012

;

44

:

651

8

.. doi:

.

66

Knouse

KA

,

Wu

J

,

Amon

A

.

Cess of megabase-scale somatic copy number variation using single-cell sequencing

.

Genome Res

.

2016

;

26

:

376

84

.. doi:

.

67

Laurie

CC

,

Laurie

CA

,

Rice

K

, et al.

Detectable clonal mosaicism from birth to old age and its human relationship to cancer

.

Nat Genet

.

2012

;

44

:

642

50

.. doi:

.

68

McConnell

MJ

,

Lindberg

MR

,

Brennand

KJ

, et al.

Mosaic copy number variation in human neurons

.

Science

.

2013

;

342

:

632

7

.. doi:

.

69

Zhou

Y

,

Bian

S

,

Zhou

X

, et al.

Single-Cell multiomics sequencing reveals prevalent genomic alterations in tumor stromal cells of human colorectal cancer

.

Cancer Cell

.

2020

;

38

:

818

28

..

e815

. doi:

.

seventy

Ludwig

LS

,

Lareau

CA

,

Ulirsch

JC

, et al.

Lineage tracing in humans enabled past mitochondrial mutations and unmarried-cell genomics

.

Jail cell

.

2019

;

176

:

1325

39

..

e1322

. doi:

.

71

Bae

T

,

Tomasini

L

,

Mariani

J

, et al.

Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis

.

Scientific discipline

.

2018

;

359

:

550

five

.. doi:

.

72

Behjati

S

,

Huch

M

,

van Boxtel

R

, et al.

Genome sequencing of normal cells reveals developmental lineages and mutational processes

.

Nature

.

2014

;

513

:

422

5

.. doi:

.

73

Coorens

THH

,

Moore

L

,

Robinson

PS

, et al.

All-encompassing phylogenies of human evolution inferred from somatic mutations

.

Nature

.

2021

;

597

:

387

92

.. doi:

.

74

Park

S

,

Mali

NM

,

Kim

R

, et al.

Clonal dynamics in early human embryogenesis inferred from somatic mutation

.

Nature

.

2021

;

597

:

393

vii

.. doi:

.

75

Asp

1000

,

Bergenstråhle

J

,

Lundeberg

J

.

Spatially Resolved Transcriptomes-Next Generation Tools for Tissue Exploration

.

Bioessays

.

2020

;

42

:

e1900221

. doi:

.

76

Chen

KH

,

Boettiger

AN

,

Moffitt

JR

, et al.

RNA imaging. Spatially resolved, highly multiplexed RNA profiling in unmarried cells

.

Science

.

2015

;

348

:

aaa6090

. doi:

.

77

Lubeck

East

,

Coskun

AF

,

Zhiyentayev

T

, et al.

Single-cell in situ RNA profiling past sequential hybridization

.

Nat Methods

.

2014

;

11

:

360

1

.. doi:

.

78

Eng

CL

,

Lawson

M

,

Zhu

Q

, et al.

Transcriptome-scale super-resolved imaging in tissues past RNA seqFISH

.

Nature

.

2019

;

568

:

235

9

.. doi:

.

79

Xia

C

,

Fan

J

,

Emanuel

G

, et al.

Spatial transcriptome profiling past MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression

.

Proc Natl Acad Sci U S A

.

2019

;

116

:

19490

nine

.. doi:

.

eighty

Ke

R

,

Mignardi

Yard

,

Pacureanu

A

, et al.

In situ sequencing for RNA assay in preserved tissue and cells

.

Nat Methods

.

2013

;

10

:

857

lx

.. doi:

.

81

Lee

JH

,

Daugharthy

ER

,

Scheiman

J

, et al.

Highly multiplexed subcellular RNA sequencing in situ

.

Science

.

2014

;

343

:

1360

3

.. doi:

.

82

Alon

S

,

Goodwin

DR

,

Sinha

A

, et al.

Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems

.

Scientific discipline

.

2021

;

371

. doi:

.

83

Wang

10

,

Allen

WE

,

Wright

MA

, et al.

Three-dimensional intact-tissue sequencing of single-cell transcriptional states

.

Science

.

2018

;

361

. doi:

.

84

Lovatt

D

,

Ruble

BK

,

Lee

J

, et al.

Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue

.

Nat Methods

.

2014

;

11

:

190

6

.. doi:

.

85

Ståhl

PL

,

Salmén

F

,

Vickovic

S

, et al.

Visualization and assay of gene expression in tissue sections by spatial transcriptomics

.

Science

.

2016

;

353

:

78

82

.. doi:

.

86

Rodriques

SG

,

Stickels

RR

,

Goeva

A

, et al.

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

.

Science

.

2019

;

363

:

1463

7

.. doi:

.

87

Stickels

RR

,

Murray

E

,

Kumar

P

, et al.

Highly sensitive spatial transcriptomics at well-nigh-cellular resolution with Slide-seqV2

.

Nat Biotechnol

.

2021

;

39

:

313

9

.. doi:

.

88

Xia

K

,

Sunday

H

,

Li

J

, et al.

Single-prison cell Stereo-seq enables jail cell type-specific spatial 1 transcriptome characterization in Arabidopsis leaves

.

bioRxiv

.

2021

.

.

89

Peng

Yard

,

Suo

South

,

Chen

J

, et al.

Spatial Transcriptome for the Molecular Annotation of Lineage Fates and Cell Identity in Mid-gastrula Mouse Embryo

.

Dev Prison cell

.

2016

;

36

:

681

97

.. doi:

.

90

Junker

JP

,

Noel

ES

,

Guryev

Five

, et al.

Genome-broad RNA Tomography in the zebrafish embryo

.

Cell

.

2014

;

159

:

662

75

.. doi:

.

91

Goodwin

South

,

McPherson

JD

,

McCombie

WR

.

Coming of age: ten years of next-generation sequencing technologies

.

Nat Rev Genet

.

2016

;

17

:

333

51

.. doi:

.

92

Brinkerhoff

H

,

Kang

ASW

,

Liu

J

, et al.

Multiple rereads of unmarried proteins at single-amino acid resolution using nanopores

.

Science

.

2021

;

374

:

1509

13

.. doi:

.

93

Fan

Ten

,

Tang

D

,

Liao

Y

, et al.

Single-cell RNA-seq analysis of mouse preimplantation embryos by third-generation sequencing

.

PLoS Biol

.

2020

;

18

:

e3001017

. doi:

.

94

Gupta

I

,

Collier

PG

,

Haase

B

, et al.

Unmarried-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells

.

Nat Biotechnol

.

2018

. doi:

.

95

Singh

M

,

Al-Eryani

G

,

Carswell

Southward

, et al.

High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes

.

Nat Commun

.

2019

;

10

:

3120

. doi:

.

96

Volden

R

,

Palmer

T

,

Byrne

A

, et al.

Improving nanopore read accuracy with the R2C2 method enables the sequencing of highly multiplexed total-length single-cell cDNA

.

Proc Natl Acad Sci U S A

.

2018

;

115

:

9726

31

.. doi:

.

97

Sedlazeck

FJ

,

Rescheneder

P

,

Smolka

M

, et al.

Accurate detection of complex structural variations using single-molecule sequencing

.

Nat Methods

.

2018

;

15

:

461

8

.. doi:

.

98

Fan

X

,

Yang

C

,

Li

W

, et al.

SMOOTH-seq: single-prison cell genome sequencing of human cells on a third-generation sequencing platform

.

Genome Biol

.

2021

;

22

:

195

. doi:

.

99

Lee

I

,

Razaghi

R

,

Gilpatrick

T

, et al.

Simultaneous profiling of chromatin accessibility and methylation on human cell lines with nanopore sequencing

.

Nat Methods

.

2020

;

17

:

1191

nine

.. doi:

.

100

Shipony

Z

,

Marinov

GK

,

Swaffer

MP

, et al.

Long-range single-molecule mapping of chromatin accessibility in eukaryotes

.

Nat Methods

.

2020

;

17

:

319

27

.. doi:

.

101

Stergachis

AB

,

Debo

BM

,

Haugen

E

, et al.

Single-molecule regulatory architectures captured past chromatin fiber sequencing

.

Scientific discipline

.

2020

;

368

:

1449

54

.. doi:

.