Single-cell transcriptomics

Last updated

Single-cell transcriptomics examines the gene expression level of individual cells in a given population by simultaneously measuring the RNA concentration (conventionally only messenger RNA (mRNA)) of hundreds to thousands of genes. [1] Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics — all previously masked in bulk RNA sequencing. [2]

Contents

Background

The development of high-throughput RNA sequencing (RNA-seq) and microarrays has made gene expression analysis a routine. RNA analysis was previously limited to tracing individual transcripts by Northern blots or quantitative PCR. Higher throughput and speed allow researchers to frequently characterize the expression profiles of populations of thousands of cells. The data from bulk assays has led to identifying genes differentially expressed in distinct cell populations, and biomarker discovery. [3]

These studies are limited as they provide measurements for whole tissues and, as a result, show an average expression profile for all the constituent cells. This has a couple of drawbacks. Firstly, different cell types within the same tissue can have distinct roles in multicellular organisms. They often form subpopulations with unique transcriptional profiles. Correlations in the gene expression of the subpopulations can often be missed due to the lack of subpopulation identification. [1] Secondly, bulk assays fail to recognize whether a change in the expression profile is due to a change in regulation or composition — for example if one cell type arises to dominate the population. Lastly, when your goal is to study cellular progression through differentiation, average expression profiles can only order cells by time rather than by developmental stage. Consequently, they cannot show trends in gene expression levels specific to certain stages. [4]

Recent advances in biotechnology allow the measurement of gene expression in hundreds to thousands of individual cells simultaneously. While these breakthroughs in transcriptomics technologies have enabled the generation of single-cell transcriptomic data, they also presented new computational and analytical challenges. Bioinformaticians can use techniques from bulk RNA-seq for single-cell data. Still, many new computational approaches have had to be designed for this data type to facilitate a complete and detailed study of single-cell expression profiles. [5]

Experimental steps

There is so far no standardized technique to generate single-cell data: all methods must include cell isolation from the population, lysate formation, amplification through reverse transcription and quantification of expression levels. Common techniques for measuring expression are quantitative PCR or RNA-seq. [6]

Isolating single cells

Fluorescence Assisted Cell Sorting workflow (FACS) Fluorescence Assisted Cell Sorting (FACS) B2.jpg
Fluorescence Assisted Cell Sorting workflow (FACS)

There are several methods available to isolate and amplify cells for single-cell analysis. Low throughput techniques are able to isolate hundreds of cells, are slow, and enable selection. These methods include:

High-throughput methods are able to quickly isolate hundreds to tens of thousands of cells. [7] Common techniques include:

Combining FACS with scRNA-seq has produced optimized protocols such as SORT-seq. [8] A list of studies that utilized SORT-seq can be found here. [9] Moreover, combining microfluidic devices with scRNA-seq has been optimized in 10x Genomics protocols. [10]

Quantitative PCR (qPCR)

To measure the level of expression of each transcript qPCR can be applied. Gene specific primers are used to amplify the corresponding gene as with regular PCR and as a result data is usually only obtained for sample sizes of less than 100 genes. The inclusion of housekeeping genes, whose expression should be constant under the conditions, is used for normalisation. The most commonly used house keeping genes include GAPDH and α-actin, although the reliability of normalisation through this process is questionable as there is evidence that the level of expression can vary significantly. [11] Fluorescent dyes are used as reporter molecules to detect the PCR product and monitor the progress of the amplification - the increase in fluorescence intensity is proportional to the amplicon concentration. A plot of fluorescence vs. cycle number is made and a threshold fluorescence level is used to find cycle number at which the plot reaches this value. The cycle number at this point is known as the threshold cycle (Ct) and is measured for each gene. [12]

Single-cell RNA-seq

RNA Seq Experiment RNA Seq Experiment.png
RNA Seq Experiment

The Single-cell RNA-seq technique converts a population of RNAs to a library of cDNA fragments. These fragments are sequenced by high-throughput next generation sequencing techniques and the reads are mapped back to the reference genome, providing a count of the number of reads associated with each gene. [13]

Normalisation of RNA-seq data accounts for cell to cell variation in the efficiencies of the cDNA library formation and sequencing. One method relies on the use of extrinsic RNA spike-ins (RNA sequences of known sequence and quantity) that are added in equal quantities to each cell lysate and used to normalise read count by the number of reads mapped to spike-in mRNA. [14]

Another control uses unique molecular identifiers (UMIs)-short DNA sequences (6–10nt) that are added to each cDNA before amplification and act as a bar code for each cDNA molecule. Normalisation is achieved by using the count number of unique UMIs associated with each gene to account for differences in amplification efficiency. [15]

A combination of both spike-ins, UMIs and other approaches have been combined for more accurate normalisation.

Considerations

A problem associated with single-cell data occurs in the form of zero inflated gene expression distributions, known as technical dropouts, that are common due to low mRNA concentrations of less-expressed genes that are not captured in the reverse transcription process. The percentage of mRNA molecules in the cell lysate that are detected is often only 10-20%. [16]

When using RNA spike-ins for normalisation the assumption is made that the amplification and sequencing efficiencies for the endogenous and spike-in RNA are the same. Evidence suggests that this is not the case given fundamental differences in size and features, such as the lack of a polyadenylated tail in spike-ins and therefore shorter length. [17] Additionally, normalisation using UMIs assumes the cDNA library is sequenced to saturation, which is not always the case. [15]

Data analysis

Insights based on single-cell data analysis assume that the input is a matrix of normalised gene expression counts, generated by the approaches outlined above, and can provide opportunities that are not obtainable by bulk.

Three main insights provided: [18]

  1. Identification and characterization of cell types and their spatial organisation in time
  2. Inference of gene regulatory networks and their strength across individual cells
  3. Classification of the stochastic component of transcription

The techniques outlined have been designed to help visualise and explore patterns in the data in order to facilitate the revelation of these three features.

Clustering

K-Means-Gaussian-data KMeans-Gaussian-data.svg
K-Means-Gaussian-data
Iris dendrogram produced using a Hierarchical clustering algorithm Iris dendrogram.png
Iris dendrogram produced using a Hierarchical clustering algorithm

Clustering allows for the formation of subgroups in the cell population. Cells can be clustered by their transcriptomic profile in order to analyse the sub-population structure and identify rare cell types or cell subtypes. Alternatively, genes can be clustered by their expression states in order to identify covarying genes. A combination of both clustering approaches, known as biclustering, has been used to simultaneously cluster by genes and cells to find genes that behave similarly within cell clusters. [19]

Clustering methods applied can be K-means clustering, forming disjoint groups or Hierarchical clustering, forming nested partitions.

Biclustering

Biclustering provides several advantages by improving the resolution of clustering. Genes that are only informative to a subset of cells and are hence only expressed there can be identified through biclustering. Moreover, similarly behaving genes that differentiate one cell cluster from another can be identified using this method. [20]

Dimensionality reduction

PCA example of Guinean and other African populations Y chromosome haplogroup frequencies PCA of Guinean and other African populations Y chromosome haplogroup frequencies.jpg
PCA example of Guinean and other African populations Y chromosome haplogroup frequencies

Dimensionality reduction algorithms such as Principal component analysis (PCA) and t-SNE can be used to simplify data for visualisation and pattern detection by transforming cells from a high to a lower dimensional space. The result of this method produces graphs with each cell as a point in a 2-D or 3-D space. Dimensionality reduction is frequently used before clustering as cells in high dimensions can wrongly appear to be close due to distance metrics behaving non-intuitively. [21]

Principal component analysis

The most frequently used technique is PCA, which identifies the directions of largest variance principal components and transforms the data so that the first principal component has the largest possible variance, and successive principle components in turn each have the highest variance possible while remaining orthogonal to the preceding components. The contribution each gene makes to each component is used to infer which genes are contributing the most to variance in the population and are involved in differentiating different subpopulations. [22]

Differential expression

Detecting differences in gene expression level between two populations is used both single-cell and bulk transcriptomic data. Specialised methods have been designed for single-cell data that considers single cell features such as technical dropouts and shape of the distribution e.g. Bimodal vs. unimodal. [23]

Gene ontology enrichment

Gene ontology terms describe gene functions and the relationships between those functions into three classes:

  1. Molecular function
  2. Cellular component
  3. Biological process

Gene Ontology (GO) term enrichment is a technique used to identify which GO terms are over-represented or under-represented in a given set of genes. In single-cell analysis input list of genes of interest can be selected based on differentially expressed genes or groups of genes generated from biclustering. The number of genes annotated to a GO term in the input list is normalised against the number of genes annotated to a GO term in the background set of all genes in genome to determine statistical significance. [24]

Pseudotemporal ordering

Graph with minimal spanning tree 7n graph with minimal spanning tree.svg
Graph with minimal spanning tree

Pseudo-temporal ordering (or trajectory inference) is a technique that aims to infer gene expression dynamics from snapshot single-cell data. The method tries to order the cells in such a way that similar cells are closely positioned to each other. This trajectory of cells can be linear, but can also bifurcate or follow more complex graph structures. The trajectory, therefore, enables the inference of gene expression dynamics and the ordering of cells by their progression through differentiation or response to external stimuli. The method relies on the assumptions that the cells follow the same path through the process of interest and that their transcriptional state correlates to their progression. The algorithm can be applied to both mixed populations and temporal samples.

More than 50 methods for pseudo-temporal ordering have been developed, and each has its own requirements for prior information (such as starting cells or time course data), detectable topologies, and methodology. [25] An example algorithm is the Monocle algorithm [26] that carries out dimensionality reduction of the data, builds a minimal spanning tree using the transformed data, orders cells in pseudo-time by following the longest connected path of the tree and consequently labels cells by type. Another example is the diffusion pseudotime (DPT) algorithm, [24] which uses a diffusion map and diffusion process. Another class of methods such as MARGARET [27] employ graph partitioning for capturing complex trajectory topologies such as disconnected and multifurcating trajectories.

Network inference

Gene regulatory network inference is a technique that aims to construct a network, shown as a graph, in which the nodes represent the genes and edges indicate co-regulatory interactions. The method relies on the assumption that a strong statistical relationship between the expression of genes is an indication of a potential functional relationship. [28] The most commonly used method to measure the strength of a statistical relationship is correlation. However, correlation fails to identify non-linear relationships and mutual information is used as an alternative. Gene clusters linked in a network signify genes that undergo coordinated changes in expression. [29]

Integration

The presence or strength of technical effects and the types of cells observed often differ in single-cell transcriptomics datasets generated using different experimental protocols and under different conditions. This difference results in strong batch effects that may bias the findings of statistical methods applied across batches, particularly in the presence of confounding. [30] As a result of the aforementioned properties of single-cell transcriptomic data, batch correction methods developed for bulk sequencing data were observed to perform poorly. Consequently, researchers developed statistical methods to correct for batch effects that are robust to the properties of single-cell transcriptomic data to integrate data from different sources or experimental batches. Laleh Haghverdi performed foundational work in formulating the use of mutual nearest neighbors between each batch to define batch correction vectors. [31] With these vectors, you can merge datasets that each include at least one shared cell type. An orthogonal approach involves the projection of each dataset onto a shared low-dimensional space using canonical correlation analysis. [32] Mutual nearest neighbors and canonical correlation analysis have also been combined to define integration "anchors" comprising reference cells in one dataset, to which query cells in another dataset are normalized. [33] . Another class of methods (e.g., scDREAMER [34] ) uses deep generative models such as variational autoencoders for learning batch-invariant latent cellular representations which can be used for downstream tasks such as cell type clustering, denoising of single-cell gene expression vectors and trajectory inference [27] .

See also

Related Research Articles

<span class="mw-page-title-main">Functional genomics</span> Field of molecular biology

Functional genomics is a field of molecular biology that attempts to describe gene functions and interactions. Functional genomics make use of the vast data generated by genomic and transcriptomic projects. Functional genomics focuses on the dynamic aspects such as gene transcription, translation, regulation of gene expression and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional "candidate-gene" approach.

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

ChIP-sequencing, also known as ChIP-seq, is a method used to analyze protein interactions with DNA. ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. It can be used to map global binding sites precisely for any protein of interest. Previously, ChIP-on-chip was the most common technique utilized to study these protein–DNA relations.

<span class="mw-page-title-main">RNA-Seq</span> Lab technique in cellular biology

RNA-Seq is a sequencing technique that uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample, representing an aggregated snapshot of the cells' dynamic pool of RNAs, also known as transcriptome.

<span class="mw-page-title-main">Single-cell analysis</span> Testbg biochemical processes and reactions in an individual cell

In the field of cellular biology, single-cell analysis and subcellular analysis is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level. The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell in a bulk population of cells at a particular condition using optical or electronic microscope. To date, due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells. Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies, enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells, as well as to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled quantifying thousands of protein across hundreds of single cells, and thus make possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.

Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.

Metatranscriptomics is the set of techniques used to study gene expression of microbes within natural environments, i.e., the metatranscriptome.

ATAC-seq is a technique used in molecular biology to assess genome-wide chromatin accessibility. In 2013, the technique was first described as an alternative advanced method for MNase-seq, FAIRE-Seq and DNase-Seq. ATAC-seq is a faster analysis of the epigenome than DNase-seq or MNase-seq.

G&T-seq is a novel form of single cell sequencing technique allowing one to simultaneously obtain both transcriptomic and genomic data from single cells, allowing for direct comparison of gene expression data to its corresponding genomic data in the same cell...

Perturb-seq refers to a high-throughput method of performing single cell RNA sequencing (scRNA-seq) on pooled genetic perturbation screens. Perturb-seq combines multiplexed CRISPR mediated gene inactivations with single cell RNA sequencing to assess comprehensive gene expression phenotypes for each perturbation. Inferring a gene’s function by applying genetic perturbations to knock down or knock out a gene and studying the resulting phenotype is known as reverse genetics. Perturb-seq is a reverse genetics approach that allows for the investigation of phenotypes at the level of the transcriptome, to elucidate gene functions in many cells, in a massively parallel fashion.

Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.

<span class="mw-page-title-main">Spatial transcriptomics</span> Range of methods designed for assigning cell types

Spatial transcriptomics is a method for assigning cell types to their locations in the histological sections and can also be used to determine subcellular localization of mRNA molecules. First described in 2016 by Ståhl et al., it has since undergone a variety of improvements and modifications.

<span class="mw-page-title-main">Trajectory inference</span>

Trajectory inference or pseudotemporal ordering is a computational technique used in single-cell transcriptomics to determine the pattern of a dynamic process experienced by cells and then arrange cells based on their progression through the process. Single-cell protocols have much higher levels of noise than bulk RNA-seq, so a common step in a single-cell transcriptomics workflow is the clustering of cells into subgroups. Clustering can contend with this inherent variation by combining the signal from many cells, while allowing for the identification of cell types. However, some differences in gene expression between cells are the result of dynamic processes such as the cell cycle, cell differentiation, or response to an external stimuli. Trajectory inference seeks to characterize such differences by placing cells along a continuous path that represents the evolution of the process rather than dividing cells into discrete clusters. In some methods this is done by projecting cells onto an axis called pseudotime which represents the progression through the process.

CITE-Seq is a method for performing RNA sequencing along with gaining quantitative and qualitative information on surface proteins with available antibodies on a single cell level. So far, the method has been demonstrated to work with only a few proteins per cell. As such, it provides an additional layer of information for the same cell by combining both proteomics and transcriptomics data. For phenotyping, this method has been shown to be as accurate as flow cytometry by the groups that developed it. It is currently one of the main methods, along with REAP-Seq, to evaluate both gene expression and protein levels simultaneously in different species.

snRNA-seq, also known as single nucleus RNA sequencing, single nuclei RNA sequencing or sNuc-seq, is an RNA sequencing method for profiling gene expression in cells which are difficult to isolate, such as those from tissues that are archived or which are hard to be dissociated. It is an alternative to single cell RNA seq (scRNA-seq), as it analyzes nuclei instead of intact cells.

<span class="mw-page-title-main">Patch-sequencing</span>

Patch-sequencing (patch-seq) is a method designed for tackling specific problems involved in characterizing neurons. As neural tissues are one of the most transcriptomically diverse populations of cells, classifying neurons into cell types in order to understand the circuits they form is a major challenge for neuroscientists. Combining classical classification methods with single cell RNA-sequencing post-hoc has proved to be difficult and slow. By combining multiple data modalities such as electrophysiology, sequencing and microscopy, Patch-seq allows for neurons to be characterized in multiple ways simultaneously. It currently suffers from low throughput relative to other sequencing methods mainly due to the manual labor involved in achieving a successful patch-clamp recording on a neuron. Investigations are currently underway to automate patch-clamp technology which will improve the throughput of patch-seq as well.

<span class="mw-page-title-main">RNA timestamp</span>

An RNA timestamp is a technology that enables the age of any given RNA transcript to be inferred by exploiting RNA editing. In this technique, the RNA of interest is tagged to an adenosine rich reporter motif that consists of multiple MS2 binding sites. These MS2 binding sites recruit a complex composed of ADAR2 and MCP. The binding of the ADAR2 enzyme to the RNA timestamp initiates the gradual conversion of adenosine to inosine molecules. Over time, these edits accumulate and are then read through RNA-seq. This technology allows us to glean cell-type specific temporal information associated with RNA-seq data, that until now, has not been accessible.

Deterministic Barcoding in Tissue for Spatial Omics Sequencing (DBiT-seq) was developed at Yale University by Rong Fan and colleagues in 2020 to create a multi-omics approach for studying spatial gene expression heterogenicity within a tissue sample. This method can used for the co-mapping mRNA and protein levels at a near single-cell resolution in fresh or frozen formaldehyde-fixed tissue samples. DBiT-seq utilizes next generation sequencing (NGS) and microfluidics. This method allows for simultaneous spatial transcriptomic and proteomic analysis of a tissue sample. DBiT-seq improves upon previous spatial transcriptomics applications such as High-Definition Spatial Transcriptomics (HDST) and Slide-seq by increasing the number of detectable genes per pixel, increased cellular resolution, and ease of implementation.

RNA velocity is based on bridging measurements to a underlying mechanism, mRNA splicing, with two modes indicating the current and future state. It is a method used to predict the future gene expression of a cell based on the measurement of both spliced and unspliced transcripts of mRNA.

References

  1. 1 2 Kanter, Itamar; Kalisky, Tomer (2015). "Single cell transcriptomics: methods and applications". Frontiers in Oncology. 5: 53. doi: 10.3389/fonc.2015.00053 . ISSN   2234-943X. PMC   4354386 . PMID   25806353.
  2. ↑ Liu, Serena; Trapnell, Cole (2016). "Single-cell transcriptome sequencing: recent advances and remaining challenges". F1000Research. 5: F1000 Faculty Rev–182. doi: 10.12688/f1000research.7223.1 . ISSN   2046-1402. PMC   4758375 . PMID   26949524.
  3. ↑ Szabo, David T. (2014). "Chapter 62 - Transcriptomic biomarkers in safety and risk assessment of chemicals". Biomarkers in Toxicology. Academic Press. pp. 1033–1038. ISBN   9780124046306.
  4. ↑ Trapnell, Cole (October 2015). "Defining cell types and states with single-cell genomics". Genome Research. 25 (10): 1491–1498. doi:10.1101/gr.190595.115. ISSN   1549-5469. PMC   4579334 . PMID   26430159.
  5. ↑ Stegle, O.; Teichmann, S.; Marioni, J. (2015). "Computational and analytical challenges in single-cell transcriptomics". Nature Reviews Genetics. 16 (3): 133–145. doi:10.1038/nrg3833. PMID   25628217. S2CID   205486032.
  6. ↑ Kolodziejczyk, Aleksandra A.; Kim, Jong Kyoung; Svensson, Valentine; Marioni, John C.; Teichmann, Sarah A. (May 2015). "The Technology and Biology of Single-Cell RNA Sequencing". Molecular Cell. 58 (4): 610–620. doi: 10.1016/j.molcel.2015.04.005 . PMID   26000846.
  7. ↑ Poulin, Jean-Francois; Tasic, Bosiljka; Hjerling-Leffler, Jens; Trimarchi, Jeffrey M.; Awatramani, Rajeshwar (1 September 2016). "Disentangling neural cell diversity using single-cell transcriptomics". Nature Neuroscience. 19 (9): 1131–1141. doi:10.1038/nn.4366. ISSN   1097-6256. PMID   27571192. S2CID   14461377.
  8. ↑ Muraro, Mauro J.; Dharmadhikari, Gitanjali; Grün, Dominic; Groen, Nathalie; Dielen, Tim; Jansen, Erik; van Gurp, Leon; Engelse, Marten A.; Carlotti, Francoise; de Koning, Eelco J. P.; van Oudenaarden, Alexander (2016-10-26). "A Single-Cell Transcriptome Atlas of the Human Pancreas". Cell Systems. 3 (4): 385–394.e3. doi:10.1016/j.cels.2016.09.002. ISSN   2405-4712. PMC   5092539 . PMID   27693023.
  9. ↑ "SORT-seq Archives". Single Cell Discoveries. Retrieved 2022-11-15.
  10. ↑ Zheng, Grace X. Y.; Terry, Jessica M.; Belgrader, Phillip; Ryvkin, Paul; Bent, Zachary W.; Wilson, Ryan; Ziraldo, Solongo B.; Wheeler, Tobias D.; McDermott, Geoff P.; Zhu, Junjie; Gregory, Mark T.; Shuga, Joe; Montesclaros, Luz; Underwood, Jason G.; Masquelier, Donald A. (2017-01-16). "Massively parallel digital transcriptional profiling of single cells". Nature Communications. 8 (1): 14049. Bibcode:2017NatCo...814049Z. doi: 10.1038/ncomms14049 . ISSN   2041-1723. PMC   5241818 . PMID   28091601.
  11. ↑ Radonić, Aleksandar; Thulke, Stefanie; Mackay, Ian M.; Landt, Olfert; Siegert, Wolfgang; Nitsche, Andreas (23 January 2004). "Guideline to reference gene selection for quantitative real-time PCR". Biochemical and Biophysical Research Communications. 313 (4): 856–862. doi:10.1016/j.bbrc.2003.11.177. ISSN   0006-291X. PMID   14706621.
  12. ↑ Wildsmith, S. E.; Archer, G. E.; Winkley, A. J.; Lane, P. W.; Bugelski, P. J. (1 January 2001). "Maximization of signal derived from cDNA microarrays". BioTechniques. 30 (1): 202–206, 208. doi: 10.2144/01301dd04 . ISSN   0736-6205. PMID   11196312.
  13. ↑ Wang, Zhong; Gerstein, Mark; Snyder, Michael (23 March 2017). "RNA-Seq: a revolutionary tool for transcriptomics". Nature Reviews. Genetics. 10 (1): 57–63. doi:10.1038/nrg2484. ISSN   1471-0056. PMC   2949280 . PMID   19015660.
  14. ↑ Jiang, Lichun; Schlesinger, Felix; Davis, Carrie A.; Zhang, Yu; Li, Renhua; Salit, Marc; Gingeras, Thomas R.; Oliver, Brian (23 March 2017). "Synthetic spike-in standards for RNA-seq experiments". Genome Research. 21 (9): 1543–1551. doi:10.1101/gr.121095.111. ISSN   1088-9051. PMC   3166838 . PMID   21816910.
  15. 1 2 Islam, Saiful; Zeisel, Amit; Joost, Simon; La Manno, Gioele; Zajac, Pawel; Kasper, Maria; Lönnerberg, Peter; Linnarsson, Sten (1 February 2014). "Quantitative single-cell RNA-seq with unique molecular identifiers". Nature Methods. 11 (2): 163–166. doi:10.1038/nmeth.2772. ISSN   1548-7091. PMID   24363023. S2CID   6765530.
  16. ↑ Kharchenko, Peter V.; Silberstein, Lev; Scadden, David T. (1 July 2014). "Bayesian approach to single-cell differential expression analysis". Nature Methods. 11 (7): 740–742. doi:10.1038/nmeth.2967. ISSN   1548-7091. PMC   4112276 . PMID   24836921.
  17. ↑ Svensson, Valentine; Natarajan, Kedar Nath; Ly, Lam-Ha; Miragaia, Ricardo J.; Labalette, Charlotte; Macaulay, Iain C.; Cvejic, Ana; Teichmann, Sarah A. (6 March 2017). "Power analysis of single-cell RNA-sequencing experiments". Nature Methods. advance online publication (4): 381–387. doi:10.1038/nmeth.4220. ISSN   1548-7105. PMC   5376499 . PMID   28263961.
  18. ↑ Stegle, Oliver; Teichmann, Sarah A.; Marioni, John C. (1 March 2015). "Computational and analytical challenges in single-cell transcriptomics". Nature Reviews Genetics. 16 (3): 133–145. doi:10.1038/nrg3833. ISSN   1471-0056. PMID   25628217. S2CID   205486032.
  19. ↑ Buettner, Florian; Natarajan, Kedar N.; Casale, F. Paolo; Proserpio, Valentina; Scialdone, Antonio; Theis, Fabian J.; Teichmann, Sarah A.; Marioni, John C.; Stegle, Oliver (1 February 2015). "Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells". Nature Biotechnology. 33 (2): 155–160. doi: 10.1038/nbt.3102 . ISSN   1087-0156. PMID   25599176.
  20. ↑ Ntranos, Vasilis; Kamath, Govinda M.; Zhang, Jesse M.; Pachter, Lior; Tse, David N. (26 May 2016). "Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts". Genome Biology. 17 (1): 112. doi: 10.1186/s13059-016-0970-8 . ISSN   1474-7596. PMC   4881296 . PMID   27230763.
  21. ↑ Pierson, Emma; Yau, Christopher (1 January 2015). "ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis". Genome Biology. 16: 241. doi: 10.1186/s13059-015-0805-z . ISSN   1474-760X. PMC   4630968 . PMID   26527291.
  22. ↑ Treutlein, Barbara; Brownfield, Doug G.; Wu, Angela R.; Neff, Norma F.; Mantalas, Gary L.; Espinoza, F. Hernan; Desai, Tushar J.; Krasnow, Mark A.; Quake, Stephen R. (15 May 2014). "Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq". Nature. 509 (7500): 371–375. Bibcode:2014Natur.509..371T. doi:10.1038/nature13173. PMC   4145853 . PMID   24739965.
  23. ↑ Korthauer, Keegan D.; Chu, Li-Fang; Newton, Michael A.; Li, Yuan; Thomson, James; Stewart, Ron; Kendziorski, Christina (1 January 2016). "A statistical approach for identifying differential distributions in single-cell RNA-seq experiments". Genome Biology. 17 (1): 222. doi: 10.1186/s13059-016-1077-y . ISSN   1474-760X. PMC   5080738 . PMID   27782827.
  24. 1 2 Haghverdi, Laleh; Büttner, Maren; Wolf, F. Alexander; Buettner, Florian; Theis, Fabian J. (1 October 2016). "Diffusion pseudotime robustly reconstructs lineage branching" (PDF). Nature Methods. 13 (10): 845–848. doi:10.1038/nmeth.3971. ISSN   1548-7091. PMID   27571553. S2CID   3594049.
  25. ↑ Saelens, Wouter; Cannoodt, Robrecht; Todorov, Helena; Saeys, Yvan (2018-03-05). "A comparison of single-cell trajectory inference methods: towards more accurate and robust tools". bioRxiv: 276907. doi: 10.1101/276907 . Retrieved 2018-03-12.
  26. ↑ Trapnell, Cole; Cacchiarelli, Davide; Grimsby, Jonna; Pokharel, Prapti; Li, Shuqiang; Morse, Michael; Lennon, Niall J.; Livak, Kenneth J.; Mikkelsen, Tarjei S.; Rinn, John L. (23 March 2017). "Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions". Nature Biotechnology. 32 (4): 381–386. doi:10.1038/nbt.2859. ISSN   1087-0156. PMC   4122333 . PMID   24658644.
  27. 1 2 Pandey, Kushagra; Zafar, Hamim (2022). "Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET". Nucleic Acids Research. 50 (15): e86. doi: 10.1093/nar/gkac412 . ISSN   0305-1048. PMC   9410915 . PMID   35639499.
  28. ↑ Wei, J.; Hu, X.; Zou, X.; Tian, T. (1 December 2016). "Inference of genetic regulatory network for stem cell using single cells expression data". 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). pp. 217–222. doi:10.1109/BIBM.2016.7822521. ISBN   978-1-5090-1611-2. S2CID   27737735.
  29. ↑ Moignard, Victoria; Macaulay, Iain C.; Swiers, Gemma; Buettner, Florian; Schütte, Judith; Calero-Nieto, Fernando J.; Kinston, Sarah; Joshi, Anagha; Hannah, Rebecca; Theis, Fabian J.; Jacobsen, Sten Eirik; de Bruijn, Marella F.; Göttgens, Berthold (1 April 2013). "Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis". Nature Cell Biology. 15 (4): 363–372. doi:10.1038/ncb2709. ISSN   1465-7392. PMC   3796878 . PMID   23524953.
  30. ↑ Hicks, Stephanie C; Townes, William F; Teng, Mingxiang; Irizarry, Rafael A (6 November 2017). "Missing data and technical variability in single-cell RNA-sequencing experiments". Biostatistics. 19 (4): 562–578. doi:10.1093/biostatistics/kxx053. PMC   6215955 . PMID   29121214.
  31. ↑ Haghverdi, Laleh; Lun, Aaron T L; Morgan, Michael D; Marioni, John C (2 April 2018). "Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors". Nature Biotechnology. 36 (5): 421–427. doi:10.1038/nbt.4091. PMC   6152897 . PMID   29608177.
  32. ↑ Butler, Andrew; Hoffman, Paul; Smibert, Peter; Papalexi, Efthymia; Satija, Rahul (2 April 2018). "Integrating single-cell transcriptomic data across different conditions, technologies, and species". Nature Biotechnology. 36 (5): 421–427. doi:10.1038/nbt.4096. PMC   6700744 . PMID   29608179.
  33. ↑ Stuart, Tim; Butler, Andrew; Hoffman, Paul; Hafemeister, Christoph; Papalexia, Efthymia; Mauck, William M III; Hao, Yuhan; Marlon, Stoeckius; Smibert, Peter; Satija, Rahul (6 June 2019). "Comprehensive Integration of Single-Cell Data". Cell. 177 (7): 1888–1902. doi:10.1016/j.cell.2019.05.031. PMC   6687398 . PMID   31178118.
  34. ↑ Shree, Ajita; Pavan, Musale Krushna; Zafar, Hamim (27 November 2023). "scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier". Nature Communications. 14 (1): 7781. doi: 10.1038/s41467-023-43590-8 . PMID   38012145.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.