Spatialomics: Past, Today and Future
This is a course-related work posted here (Please keep in mind that I am not expert, yet, in this field. If you are further interested in, I strongly recommend you to read main articles referred in the text.).
Abstract
Sequencing produces plethora of information. However, in RNA sequencing it is required to disassociate cells from their microenvironment. This detachment causes the loss of spatial -location dependent- information of the cells. On the other hand, in situ and spatial sequencing enable transcriptome profiling while preserving the spatial information. Besides, recently developed algorithms combine the data accumulated through in situ approaches and sequencing to provide newer insights regarding the molecular function, trajectories of the cells and the underlying mechanisms of the diseases. In this post, both the brief history of this recently emerged field and the recent advancements/future in/of spatialomics approaches together with the technologies behind are reviewed.
Keywords: spatial sequencing, in situ sequencing, transcriptomics, single cell sequencing, FISH
Introduction
Since spatialomics is a relatively new field, it is built on previously studied fields such as sequencing and in situ hybridization. In this section, the background of spatialomics is discussed.
1.1 Brief introduction to RNA Sequencing
RNA Sequencing (RNA-Seq) has been used to elucidate the expression pattern of the genomic loci in certain cells/tissues. RNA-Seq has several advantages compared to cDNA microarrays. These advantages are no requirement of pre-defined probe sets, having increased sensitivity, and therefore additional applications of this relatively newer technology on spliced isoform identification, detection of protein-RNA domains and de novo assembly of genomes (Nookaew et al., 2012; Z. Wang, Gerstein, & Snyder, 2009). RNA sequencing technology has been mostly used to detect gene expression changes between different experimental conditions, tissues or other cell types at transcriptomic level.
Brief Definitions
Gene: It is known as the smallest meaningful/functional unit of a heredity.
Transcript: Complementary copies of a DNA template made of single stranded RNA synthesized by DNA transcription. The relevant template might have more than one RNA transcript.
mRNA: messenger RNA, which is supposed to carry the functional information from a gene to be translated into protein after processing. Not all of them required to be translated into protein.
Protein: The molecule in the cell that do the function, which is translated from processed mRNA.
Sequencing: The genetic information is encoded in 4 letters/nucleotides: A, T in DNA (and U in RNA), C, and G for living systems. Sequencing is biological decoding mechanism to reveal the identity of each gene/transcript.
-omics: The term used to refer the collection, quantification and the comprehensive characterization of biological molecules such as DNA, RNA, protein or metabolome (collectively).
Transcriptomics: The field interested in the collection of all RNAs -mainly mRNAs- of the cells/tissues and their relationship with each other and their role/expression difference in various conditions such as diabetes.
RNA sequencing: A technique which makes the dream of high throughput transcriptomics come true providing various information regarding the molecular dynamics of the cells in differing conditions.
scRNA-seq: RNA sequencing of a single cell.
ISH-probe: A piece of sequence carrying modified labelling molecule to detect the location of mRNA/DNAs in in situ approaches.
ISH: In situ hybridization. A technique uses probes to detect and quantify the targeting molecule.
FISH: Fluorescent in situ hybridization. It is a type of ISH using fluorescent labelled probes.
Spatialomics: The relatively newer sub-field of omics interested in preserving the spatial information for omics data.
1.1.1 single cell RNA sequencing
Although RNA Sequencing itself is a powerful technology, not all the cells are equal, even in the same tissue (Levsky and Singer, 2013). Despite different clusters of cells having different cell structure and dynamics, therefore having different gene expression profile, are present in the same tissue, bulk RNA-seq measures the average expression of the genes/transcripts in the given tissue/sample. However, with the emergence of single cell whole-transcriptome sequencing technology (scRNA-seq), heterogeneity of the cells is resolved at single cell level by providing biologically meaningful information regarding the cell to cell variability (Tang et al., 2009). scRNA-seq is improved our understanding of cells/conditions in many ways, especially in stem cell research, development, (reviewed by Kretzschmar and Watt, 2012; Hoppe et al., 2014; Baccin et al., 2020), cancer(Almendro et al., 2014), and neuronal context (Codeluppi et al., 2018).
The steps followed in general for scRNA-seq are capturing/isolating the single cells, lysis, reverse transcription of the RNAs to produce cDNAs, preamplification of these followed by library preparation and sequencing. Unfortunately, the spatial information is lost during these processes (Figure 1).
The detailed information regarding the bulk/scRNA seq was discussed in related review articles (Shapiro et al., 2013; Junker and Qudenaarden, 2014; Kolodziejczyk et al., 2015; Wu et al., 2014; Hoppe et al., 2014; Shwartzan and Tanay, 2015; Grün and Ouneaarden, 2015; Stark et al., 2019; Zhang, 2019) and beyond the scope of spatialomics focused review.
1.2 Brief introduction to in situ hybridization
In situ hybridization (ISH) preserves location information of a target molecule (e.g. mRNA, DNA). ISH is a technique which uses complementary (nucleic acid strand) probes to detect the signal derived from the hybridization of the probe and the target molecule. According to NCBI database (NCBI>Probe>ISH), probes are made of either DNA (e.g. dsDNA, ssDNA), RNA or synthetic oligonucleotides (e.g. PNA, LNA). These probes can carry either of radioactive isotopes (e.g. 32P, 35S, 3H) or non-radioactive (e.g. biotin, digoxigenin, fluorescent dye) labels. The detection of the target molecules is done by visualization of the probes.
1.2.1 FISH (Fluorescent in situ hybridization)
ISH with fluorescently labelled probes (also known as FISH) are one of the most commonly used techniques to detect RNA molecules while preserving the spatial information. Fluorescently labelled riboprobes allow the assessment of gene expression of the transcript of interest(s) via visualization through fluorescent microscope (Femino et al., 1998). It is superior in terms of higher sensitivity and specificity for the target molecule by recognizing them at the site of action, in situ, thanks to improved, super resolution, fluorescent microscopy. These and additional various aspects of fluorescent microscopy were discussed in elsewhere (Cui et al., 2016) and beyond the scope of this spatial transcriptomics article.
This method is even applicable on thick tissues thanks to super resolution microscopy and swellable gel technology combined in Expension fluorescent in situ hybridization (ExFISH) (Chen et al., 2016).
1.3 Brief introduction to Mass Spectrometry
will be coming soon…
(What is mass spec? How does it work? What is its applications? More specifically for spatial transcriptomics?)
Methods and Technologies
In this section, the methodologies used for different spatialomics approaches and the technologies developed based on these methods will be discussed. The brief summary of the discussion can be found in Table 1.
2.1 Methods Used
2.1.1 Padlock probes and Rolling Circle Amplification
Padlock probes are integrated to target molecules after synthesizing the cDNA, and then circulated. Padlock probes together with rolling circle amplification (RCA) to amplify the fluorescent signal by isothermal amplification method provides enhancement of the signal for imaging as applied in FISSEQ (Lee et al., 2014). To distinguish the changes at single nucleotide level, RCA is required.
2.1.2 Branched FISH
Branched DNA (bDNA) amplification applied on fixed cells/tissues in MERFISH.v2 (Xia et al., 2019) can be used to overcome the limitations of distinguishing relatively fade signals, challenges of imaging small RNAs. Main advantage of this technique is the improvement in the variation of different spots, so that one of the limitations of previous MERFISH can be overcome. However, bDNA is not suitable for multiplexing.
2.1.3 LCM
Laser capture micro dissection (LCM) is a method used to record the spatial information. It is used in Geo-Seq (Chen et al., 2017). Main advantage of this method is providing high spatial resolution. Main disadvantage of this method is the requirement of advanced equipment.
2.1.4 RNA barcoding
It is a method used in spatial transcriptomics (Stahl et al., 2016) and Slide-Seq (Rodriques et al., 2019) approaches. In this method, mRNAs captured from freshly frozen tissues are barcoded before the isolation of the cells by capturing the mRNAs directly from the target tissue. The requirement of freshly frozen fixed tissues and limited resolution due to dependency on the size of the array and the spacing are two main disadvantages of these methods by limiting the tissue size this method can be applied.
2.1.5 Spectral barcoding with LPs
The number of the laser particles used in mass-spec based imaging is limited. The laser particles (LPs) having narrowband laser emission spectra might improve throughput and multiplexing of single cell analysis (reviewed by Sheldon et al., 2019).
2.1.6 PLAYR
Proximity Ligation Assay for RNA (PLAYR) (Frei et al., 2016) is developed to detect RNA transcript in highly multiplexed manner by flow and mass spectrometry. After cell fixation process, PLAYR probe pair is hybridized followed by RCA and later detection. Although background binding is low and so specificity is high, considering FISH, and multiplexing with protein quantification is doable, throughput is very limited.
2.1.7 Slide-Seq
details will be coming soon… (https://science-sciencemag-org.libproxy1.nus.edu.sg/content/363/6434/1463.long)
All the methods have advantages and disadvantages and most of the case: mass-spectrometry based methods suffers from limited number of the probes, and fluorescent imaging-based methods suffer from resolution for higher dimensions of the probes.
Main approaches used for spatialomics studies are shown in Figure 2.
2.2 ISH probe-based methods
2.2.1 smFISH
smFISH stands for single molecular fluorescently labelled in situ, at the point of transcription, hybridization (Raj et al., 2008). smFISH uses 20bp probes of oligonucleotides, each of them labelled with single fluorophore. Accumulation of the signal coming from mRNA targets hybridized with fluorescent probes, as diffraction limited spots, provides visualization of the them individually under the fluorescent microscope (Lubeck and Cai, 2012).
In fact, however, it is not possible to run more than 3 or 4 fluorophores to detect the transcripts, which limits the number of transcripts (depends on the number of fluorophores used) suitable to detect simultaneously by using smFISH. On the other hand, these limitations partially overcome by combinatorial labelling approach (Levsky et al., 2002). They succeeded to detect 2n-1 transcripts when n number of fluorophores used by grouping the fluorophore libraries by coupling them with the number of fluorophores available. Super resolution microscopy combined with spatial barcodes is improved the number of transcripts can be detected even more by being able to resolve different regions of the same mRNA molecule (Lubeck and Cai, 2012).
2.2.1.1 seqFISH
Sequential hybridization by sequential fluorescent in situ hybridization increases the number of the transcripts can be detected by smFISH up to fh (f: #fluorophores, h: hybridization cycles) (Lubeck et al., 2014). After each rounds of hybridization, images of spatial information are recorded, then followed by probe stripping and repeating these steps for each hybridization cycle, which contributes information regarding the temporal information as well.
However, due to stochasticity of the probe hybridization process, there is a high change of assignment of the probes to wrong target when the hybridizations do not work well.
2.2.2 MERFISH
2.2.2.1 MERFISH v.1
In smFISH, each hybridization cycle contributes to increased error rate for assignment of the genes when the hybridization cycle number increases, which is a limiting step. Multiplex error-robust fluorescent in situ hybridization (Chen et al., 2015) overcomes this by barcoding different genes far-enough. It improves costly process of synthesis of multiple probes and slow process of hybridization rounds. Thanks to two-stage hybridization, which provides the detection of 103 transcripts with less than 15 rounds of hybridizations, the technique can be applied to single cells of tissue culture. However, it requires long, 1000, RNA species to be probed.
2.2.2.2 MERFISH v.2 (w/branch DNA amplification)
MERFISH with branched DNA amplification aims to increase the robustness of the previous version it by using bDNA (Xia et al., 2019). It is important to differentiate the spots from each other based on the brightness of each spot and the specific algorithms developed by the authors. bDNA enables to differentiate the various spots. MERFISH.v2 is improved to detect short RNA molecules as well.
2.3 Sequencing based approaches:
2.3.1 Microtomy sequencing
It is used for spatially resolved mRNA sequencing. RNAs derived from thin tissue sections were cryopreserved and sequenced.
2.3.2 LCM-Seq
Laser Capture Microdissection (LCM) (Nichterwitz et al. 2018) combined with RNA sequencing uses micro dissected frozen tissues, and sequence them with polyA-based sequencing. It is applicable on single cells.
2.3.3 TOMO-Seq
Tomography based imaging of the transcriptome (TOMO-Seq), uses microtomy based approaches, cryosections of the cells lysed in TRIzol barcoded with oligo primers (Junker et al., 2014). Then cDNA is linearly amplified via in vitro transcription (IVT). It lacks single cell resolution. It might be useful to generate 2D/3D maps of expression space.
2.3.4 TIVA
Transcriptome in vivo analysis (TIVA) uses photoactivable tags containing biotin applied on live cells (Lovatt et al., 2014). It takes the advantage of Fluorescence Resonance Energy Transfer (FRET) to uptake and decode the tags.
2.3.5 In situ single cell RNA-Sequencing
Although, single cell sequencing provides plethora of information regarding the subpopulations of the cells in a given context -the single cell niche-, the interaction of the cells with its microenvironments is also crucial to define its role, understand its function, and reveal cellular dynamics (Donati, 2015). The expression dynamics of transcriptome of the cells and their microenvironment, which might be defined as spatial information, is preserved during in situ RNA sequencing studies.
2.3.5.1 In situ RNA seq
Although combinatorial approaches used so far improved the number of transcripts detected by smFISH, the main limitation of these is the pre-defined probe selection, just like in the case of microarray vs. RNA-seq. Therefore, to overcome the detection limit, it is necessary to have a method that is independent of pre-selection of genes. High throughput version of spatially resolved RNA-Seq is achieved by in situ RNA sequencing (Ke et al., 2013). Instead of using flowcell -a platform used to sequence the samples and take the readouts- for detection, the tissue itself is used. It follows the similar procedures with RNA sequencing: cDNA synthesis and amplification of the cDNAs.
2.3.5.2 FISSEQ
Fluorescent In situ RNA Sequencing (FISSEQ) provides the detection of transcripts in vivo -within the cell- and in situ -where they reside- by using the amplification of complementary DNA sequences (cDNAs) via rolling circle amplification (Lee et al., 2014).
2.3.7.6 STARMap
Combination of amplification of RNAs, optical clearing of the tissues and fluorescent based visualization is used to distinguish around 103 single cells in the brain by using STAR-Map (spatially resolved transcript amplicon readout mapping) (Wang et al., 2018). By using hydrogel- tissue chemistry, they demonstrated the RNA sequencing in 3D intact tissue.
2.3.6 Spatial Transcriptomics
Positional barcoding by Unique Molecular Identifier (UMI) for individual tissue sections preserves the spatial information. This is achieved in spatial transcriptomic approach (Stahl et al., 2016). In this method, it is showed that positional information can be easily kept intact, and the requirement of single cell isolation might be obsolete for spatial sequencing.
2.4 Spatial Mapping
Although scRNA-seq generates lots of data for expression profile of many genes, it is challenging to preserve the positional information of the cells in the given tissue due to disassociation process. Therefore, instead of doing in situ sequencing like approaches, and use the previously produced sc-RNA-seq data, some researchers combined sc-transcriptomics data with tissue reference maps which depends on selected subset of markers (Durruthy-Durruthy et al., 2014; Halpern et al., 2017). It is accomplished by mapping the selected marker genes to RNA in situ data to build 3D model of the tissues. It is noteworthy that inferring based methods take the advantage of single cell transcriptomic approaches and spatial information without interfering each other, by using the power of bioinformatics algorithms.
Conclusions and Future Perspectives for spatialomics approaches
Birth of the omics was a game changer and a bridge between in the field of biology and the computational sciences (Hieter and Boguski, 1997). High throughput RNA sequencing technology revolutionized the field of transcriptomics. Yet RNA-seq at single cell resolution contributes to decipher underlying mechanism of the cells/pathways/functions by revealing the heterogeneity of “supposed to be same”. However, all these are relevant in the spatial context. Therefore, spatialomics is involved to solve challenges regarding the mapping of spatial information and the transcriptomics data. However, the field is limited by resolution of increased number of fluorescent signals and limited number of laser particles available (Spitzer et al., 2016).
Spatialomics is a relatively newer field. There are always better techniques for imaging due to the limitations caused by either resolution power, algorithms, equipment or the reagents used. It is in fact required to develop better algorithms to enable these techniques to work efficiently together with simpler and robust technologies.
There are probe based and sequencing based techniques available. These techniques are using different methods to detect/amplify the signal. They differ in terms of application on cell/tissue (e.g. fixed, in vivo) and their strength and limitations (e.g. high vs. low throughput, detection range). In addition to these, newer bioinformatics-based mapping methods are developed. Although these methods lack high-throughput, recently Kenneth Hu and colleagues (under review) developed ZipSeq, which enables high throughput mapping of live single cells in certain tissues to their transcriptome.
Although, spatial resolution is an important concept, temporal resolution is also crucial to relate the state of the gene expression to its function in the given cell/tissue. Therefore, combination of temporal/nascent RNA sequencing approaches (Shah et al., 2018) together with their spatial information might improve our understanding of cellular context, disease conditions and the life itself.
Besides, the combination of spatial approaches with microfluidic systems (Stark et al., 2019; Moncada et al., 2019) will provide easy to manipulate, cheaper and faster processing.
Acknowledgements
I would like to thank my friend/colleague A. Ömer Aydar for inspiring me to write a review about spatialomics for one of the courses I have been taking during this semester.
Bonus: Up-to-date review about “Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics”
References
Almendro, V., Cheng, Y.-K., Randles, A., Itzkovitz, S., Marusyk, A., Ametller, E., Gonzalez-Farre, X., Muñoz, M., Russnes, H. G., Helland, Å., Rye, I. H., Borresen-Dale, A.-L., Maruyama, R., Oudenaarden, A. van, Dowsett, M., Jones, R. L., Reis-Filho, J., Gascon, P., Gönen, M., … Polyak, K. (2014). Inference of Tumor Evolution during Chemotherapy by Computational Modeling and In Situ Analysis of Genetic and Phenotypic Cellular Diversity. Cell Reports, 6(3), 514–527. https://doi.org/10.1016/j.celrep.2013.12.041
Baccin, C., Al-Sabah, J., Velten, L., Helbling, P. M., Grünschläger, F., Hernández-Malmierca, P., Nombela-Arrieta, C., Steinmetz, L. M., Trumpp, A., & Haas, S. (2020). Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nature Cell Biology, 22(1), 38–48. https://doi.org/10.1038/s41556-019-0439-6
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015). Spatially resolved, highly multiplexed RNA profiling in single cells. Science, 348(6233), aaa6090. https://doi.org/10.1126/science.aaa6090
Codeluppi, S., Borm, L. E., Zeisel, A., La Manno, G., van Lunteren, J. A., Svensson, C. I., & Linnarsson, S. (2018). Spatial organization of the somatosensory cortex revealed by osmFISH. Nature Methods, 15(11), 932–935. https://doi.org/10.1038/s41592-018-0175-z
Femino, A. M., Fay, F. S., Fogarty, K., & Singer, R. H. (1998). Visualization of Single RNA Transcripts in Situ. Science, 280(5363), 585. https://doi.org/10.1126/science.280.5363.585
Frei, A. P., Bava, F.-A., Zunder, E. R., Hsieh, E. W. Y., Chen, S.-Y., Nolan, G. P., & Gherardini, P. F. (2016). Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nature Methods, 13(3), 269–275. https://doi.org/10.1038/nmeth.3742
Halpern, K. B., Shenhav, R., Matcovitch-Natan, O., Tóth, B., Lemze, D., Golan, M., Massasa, E. E., Baydatch, S., Landen, S., Moor, A. E., Brandis, A., Giladi, A., Stokar-Avihail, A., David, E., Amit, I., & Itzkovitz, S. (2017). Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature, 542(7641), 352–356. https://doi.org/10.1038/nature21065
Ke, R., Mignardi, M., Pacureanu, A., Svedlund, J., Botling, J., Wählby, C., & Nilsson, M. (2013). In situ sequencing for RNA analysis in preserved tissue and cells. Nature Methods, 10(9), 857–860. https://doi.org/10.1038/nmeth.2563
Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Yang, J. L., Ferrante, T. C., Terry, R., Jeanty, S. S. F., Li, C., Amamoto, R., Peters, D. T., Turczyk, B. M., Marblestone, A. H., Inverso, S. A., Bernard, A., Mali, P., Rios, X., Aach, J., & Church, G. M. (2014). Highly Multiplexed Subcellular RNA Sequencing in Situ. Science, 343(6177), 1360. https://doi.org/10.1126/science.1250212
Levsky, J. M., Shenoy, S. M., Pezo, R. C., & Singer, R. H. (2002). Single-Cell Gene Expression Profiling. Science, 297(5582), 836. https://doi.org/10.1126/science.1072241
Levsky, J. M., & Singer, R. H. (2003). Gene expression and the myth of the average cell. Trends in Cell Biology, 13(1), 4–6. https://doi.org/10.1016/S0962-8924(02)00002-8
Lubeck, E., & Cai, L. (2012). Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nature Methods, 9(7), 743–748. https://doi.org/10.1038/nmeth.2069
Moncada, R., Barkley, D., Wagner, F., Chiodin, M., Devlin, J. C., Baron, M., Hajdu, C. H., Simeone, D. M., & Yanai, I. (2020). Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nature Biotechnology. https://doi.org/10.1038/s41587-019-0392-8
Nichterwitz, S., Chen, G., Aguila Benitez, J., Yilmaz, M., Storvall, H., Cao, M., Sandberg, R., Deng, Q., & Hedlund, E. (2016). Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nature Communications, 7(1), 12139. https://doi.org/10.1038/ncomms12139
Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A., & Tyagi, S. (2008). Imaging individual mRNA molecules using multiple singly labeled probes. Nature Methods, 5(10), 877–879. https://doi.org/10.1038/nmeth.1253
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., Mollbrink, A., Linnarsson, S., Codeluppi, S., Borg, Å., Pontén, F., Costea, P. I., Sahlén, P., Mulder, J., Bergmann, O., … Frisén, J. (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 353(6294), 78. https://doi.org/10.1126/science.aaf2403
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., Lao, K., & Surani, M. A. (2009). MRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods, 6(5), 377–382. https://doi.org/10.1038/nmeth.1315
Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., Nolan, G. P., Bava, F.-A., & Deisseroth, K. (2018). Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science, 361(6400), eaat5691. https://doi.org/10.1126/science.aat5691
Xia, C., Babcock, H. P., Moffitt, J. R., & Zhuang, X. (2019). Multiplexed detection of RNA using MERFISH and branched DNA amplification. Scientific Reports, 9(1), 7721. https://doi.org/10.1038/s41598-019-43943-8
Chen, Xiaoyin, Sun, Y.-C., Zhan, H., Kebschull, J. M., Fischer, S., Matho, K., Huang, Z. J., Gillis, J., & Zador, A. M. (2019). High-Throughput Mapping of Long-Range Neuronal Projection Using In Situ Sequencing. Cell, 179(3), 772–786.e19. https://doi.org/10.1016/j.cell.2019.09.023
Chen, Xingqi, Shi, C., Yammine, S., Göndör, A., Rönnlund, D., Fernandez-Woodbridge, A., Sumida, N., Widengren, J., & Ohlsson, R. (2014). Chromatin in situ proximity (ChrISP): Single-cell analysis of chromatin proximities at a high resolution. BioTechniques, 56(3), 117–124. https://doi.org/10.2144/000114145
Crosetto, N., Bienko, M., & van Oudenaarden, A. (2015). Spatially resolved transcriptomics and beyond. Nature Reviews Genetics, 16(1), 57–66. https://doi.org/10.1038/nrg3832 (one of main review articles in this field)
Cui, C., Shu, W., & Li, P. (2016). Fluorescence In situ Hybridization: Cell-Based Genetic Diagnostic and Research Applications. Frontiers in Cell and Developmental Biology, 4, 89. https://doi.org/10.3389/fcell.2016.00089
Donati, G. (2016). The niche in single-cell technologies. Immunology & Cell Biology, 94(3), 250–255. https://doi.org/10.1038/icb.2015.107
Gao, H., Zhang, K., Teng, X., & Li, J. (2019). Rolling circle amplification for single cell analysis and in situ sequencing. TrAC Trends in Analytical Chemistry, 121, 115700. https://doi.org/10.1016/j.trac.2019.115700
Grün, D., & Oudenaarden, A. van. (2015). Design and Analysis of Single-Cell Sequencing Experiments. Cell, 163(4), 799–810. https://doi.org/10.1016/j.cell.2015.10.039
Hieter, P., & Boguski, M. (1997). Functional Genomics: It’s All How You Read It. Science, 278(5338), 601–602. https://doi.org/10.1126/science.278.5338.601
Hoppe, P. S., Coutu, D. L., & Schroeder, T. (2014). Single-cell technologies sharpen up mammalian stem cell research. Nature Cell Biology, 16(10), 919–927. https://doi.org/10.1038/ncb3042
Hu, K. H., Eichorst, J. P., McGinnis, C. S., Patterson, D. M., Chow, E. D., Kersten, K., Jameson, S. C., Gartner, Z. J., Rao, A. A., & Krummel, M. F. (2020). ZipSeq: Barcoding for Real-time Mapping of Single Cell Transcriptomes. BioRxiv, 2020.02.04.932988. https://doi.org/10.1101/2020.02.04.932988
In Situ Hybridization (ISH). (n.d.). Retrieved March 17, 2020, from https://www-ncbi-nlm-nih-gov.libproxy1.nus.edu.sg/probe/docs/techish/
Junker, J. P., Noël, E. S., Guryev, V., Peterson, K. A., Shah, G., Huisken, J., McMahon, A. P., Berezikov, E., Bakkers, J., & Oudenaarden, A. van. (2014). Genome-wide RNA Tomography in the Zebrafish Embryo. Cell, 159(3), 662–675. https://doi.org/10.1016/j.cell.2014.09.038
Junker, J. P., & Oudenaarden, A. van. (2014). Every Cell Is Special: Genome-wide Studies Add a New Dimension to Single-Cell Biology. Cell, 157(1), 8–11. https://doi.org/10.1016/j.cell.2014.02.010
Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015). The Technology and Biology of Single-Cell RNA Sequencing. Molecular Cell, 58(4), 610–620. https://doi.org/10.1016/j.molcel.2015.04.005
Kretzschmar, K., & Watt, F. M. (2012). Lineage Tracing. Cell, 148(1), 33–45. https://doi.org/10.1016/j.cell.2012.01.002
Kwok, S. J. J., Martino, N., Dannenberg, P. H., & Yun, S.-H. (2019). Multiplexed laser particles for spatially resolved single-cell analysis. Light: Science & Applications, 8(1), 74. https://doi.org/10.1038/s41377-019-0183-5
Lein, E., Borm, L. E., & Linnarsson, S. (2017). The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science, 358(6359), 64. https://doi.org/10.1126/science.aan6827
Moor, A. E., & Itzkovitz, S. (2017). Spatial transcriptomics: Paving the way for tissue-level systems biology. Current Opinion in Biotechnology, 46, 126–133. https://doi.org/10.1016/j.copbio.2017.02.004 (one of main review articles in this field)
Nichterwitz, S., Benitez, J. A., Hoogstraaten, R., Deng, Q., & Hedlund, E. (2018). LCM-Seq: A Method for Spatial Transcriptomic Profiling Using Laser Capture Microdissection Coupled with PolyA-Based RNA Sequencing. In I. Gaspar (Ed.), RNA Detection: Methods and Protocols (pp. 95–110). Springer New York. https://doi.org/10.1007/978-1-4939-7213-5_6
Schwartzman, O., & Tanay, A. (2015). Single-cell epigenomics: Techniques and emerging applications. Nature Reviews Genetics, 16(12), 716–726. https://doi.org/10.1038/nrg3980
Shapiro, E., Biezuner, T., & Linnarsson, S. (2013). Single-cell sequencing-based technologies will revolutionize whole-organism science. Nature Reviews Genetics, 14(9), 618–630. https://doi.org/10.1038/nrg3542
Spitzer, M. H., & Nolan, G. P. (2016). Mass Cytometry: Single Cells, Many Features. Cell, 165(4), 780–791. https://doi.org/10.1016/j.cell.2016.04.019
Stark, R., Grzelak, M., & Hadfield, J. (2019). RNA sequencing: The teenage years. Nature Reviews Genetics, 20(11), 631–656. https://doi.org/10.1038/s41576-019-0150-2
Wu, A. R., Neff, N. F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M. E., Mburu, F. M., Mantalas, G. L., Sim, S., Clarke, M. F., & Quake, S. R. (2014). Quantitative assessment of single-cell RNA-sequencing methods. Nature Methods, 11(1), 41–46. https://doi.org/10.1038/nmeth.2694
Zhang, H. (2019). The review of transcriptome sequencing: Principles, history and advances. IOP Conference Series: Earth and Environmental Science, 332, 042003. https://doi.org/10.1088/1755-1315/332/4/042003
