With the advancement of technology, spatial transcriptomes (spatial transcriptomes) have become a "hot commodity" in throughput sequencing technology in recent years. The reason for
is that the spatial transcriptome function is "multiple killing" and it can not only provide gene expression spectrum data (approximately single-cell sequencing data), but also provide sequencing location information, and even the corresponding pathology image data is visible because of it.
However, the gene expression profile data of spatial transcriptome technology (especially high-resolution spatial transcriptome technology) cannot be "used" and there is still a lot of noise.
They either come from low throughput sequencing depths (located at each sequencing site) or from those additional experimental steps performed to preserve sequencing locations. These noises form a natural barrier when researchers extract valuable information from spatial transcriptome data.
To correct the noise in the gene expression profile data of the spatial transcription group, the Southwestern Medical Center of the University of Texas in the United States recently cooperated with the team at the University of Texas Arlington to invent a method called "Sprod". They use information about spatial location and pathological images to solve the above problems.
On August 4, the related paper was published on Nature Methods Nature Methods [1].
Figure丨Related papers (Source: Nature Methods)
The co-first author of this paper is the Quantitative Biology Research Center of the University of Texas Southwest Medical Center Data scientist Doctor Wang Yunguan and postdoctoral researcher Song Bing, the paper co-corresponding author Assistant Professor Wang Tao of the Center for Quantitative Biology Research at the University of Texas Southwest Medical Center and Professor Wang Li of the University of Texas Arlington. Other main authors of the paper include Professor Xie Yang, Professor Xiao Guanghua, and Assistant Professor Wang Shidan.
Reviewer commented on the paper: "With the popularity of spatial sequencing technology, it is very important to develop tools that can effectively process and analyze such data sets. Sprod is a new method in the right direction. Using spatial information and relationships, it is particularly attractive to denoising spatial transcriptome gene expression profile data."
Figure 丨Extensive noise in spatial parsed transcriptome data (Source: Nature Methods)
The team found in spatial transcriptome data that in its pathological images and overall transcriptional profile, if the sites are similar and the sequencing positions are adjacent, the gene transcriptional profile will be similar. Moreover, this transcriptional similarity is proportional to the degree of similarity between sites.
Based on this principle, the researchers used Sprod to establish a cryptographic model, and used the analysis of the spatial distances and gene expression profile characteristics of different sequencing sites to put the sequencing sites into the cryptographic map. The expression spectrum information of the spatial transcriptome flows based on the Cain map, thereby realizing noise reduction of the expression spectrum data.
Talking about the application of this method, Wang Yunguan said, "Sprod can be widely used in various spatial transcriptome technologies, such as Visium, Slide-Seq, HDST, Seq-Scope, etc. The resolution is proportional to the noise of the expression spectrum data, that is, the higher the resolution of the technology type, the greater the noise, so Sprod also plays a greater value in it."
Figure丨Spatial parsing transcriptome data for denoising (Source: Nature Methods)
The team first discussed Sprod on simulation data (simulation data) The accuracy of the
On the test dataset, the researchers generated data similar to spatial transcriptome data and artificially added noise.After applying Sprod, they found that at different noise levels, Sprod removed at least 85% of the noise.
"After the verification obtained satisfactory results, we optimized the parameters and finally verified and applied the algorithm on the data sets from various spatial transcriptome technology." Wang Yunguan said.
Figure丨Using expression data corrected by sprod, the inference of intercellular communication is more accurate (Source: Nature Methods)
This paper focuses on the application of Sprod on real data. The researchers tested data from multiple platforms including Visium, Slide-Seq, HDST and Seq-Scope, and found that sprod can enhance the reliability of differential expression analysis based on data noise reduction, and enable downstream quasi-timed and cell communication analysis to more realistically respond to corresponding biological information.
Wang Yunguan believes that just as the "booming" development of single-cell sequencing technology and application after 2014, spatial transcriptome technology will be a popular technology in the next few years.
He said: "Sprod is highly versatile and can correct data noise from different sources. Therefore, Sprod can be applied in most current and possible spatial transcriptome technologies in the future. Moreover, the data is preprocessed in the initial stage of data analysis to improve the reliability of the entire data analysis process."
Figure丨Wang Yunguan (Source: Wang Yunguan)
Wang Yunguan graduated from the Department of Bioengineering of Dalian University of Technology undergrad. After that, he studied immunology at the University of Cincinnati in the United States, and Bioinformatics in his master's and doctoral studies at the University of Cincinnati in the United States, respectively.
Previously, he worked as a computing biologist in the System Pharmacy Laboratory of Harvard Medical School, and during this period he developed quality detection and data analysis methods for repeated immunofluorescence staining data. After joining the Southwest Medical Center, in cooperation with Associate Professor Zhu Hao, Wang Yunguan used single cells to analyze the gene expression of stem cells in structural area 2 and developed tissue positioning system image analysis software to perform large-scale system image processing [2]. Through these analyses, hepatocytes in zone 2 mediate dynamic equilibrium between hepatocytes.
In addition, in cooperation with Mouping's research group, he also used single-cell transcriptome analysis to reveal the important role of JAK-STAT inducing cell transformation into stem cell-like phenotypes and pluripotency in prostate cancer , resulting in an important role in drug resistance [3].
In recent years, Wang Tao Laboratory (Tao Wang Lab) has achieved a series of fruitful results in computational immunology. Professor Wang Tao's research mainly focuses on studying immune receptor-antigen binding, comprehensive analysis of single-cell transcriptome and immune receptor sequences, and using spatial transcriptome data to study immunology issues. At present, the Center for Quantitative Biology Research has multiple postdoctoral recruitment positions (qbrc.swmed.edu/labs/wanglab, qbrc.swmed.edu/labs/xielab, qbrc.swmed.edu/labs/xiaolab), and talents in bioinformatics from various majors are welcome to join.
Reference:
1.Wang, Y., Song, B., Wang, S. et al. Sprod for de-noising spatially resolved transcriptomics data based on position and image information. Nature Methods 19, 950–958 (2022). https://doi.org/10.1038/s41592-022-01560-w
2.Wei,Y.,Wang, Y. et al. Liver homeostasis is maintained by midlobular zone hepatocytes.Sceinece 371, 6532(2021). DOI: 10.1126/science.abb1625
3.Deng, S., Wang, C., Wang, Y. et al. Economic JAK–STAT activation enables the transition to a stem-like and multilineage state conferring AR-targeted therapy resistance. Nature Cancer 3, 1071–1087 (2022). https://doi.org/10.1038/s43018-022-00431-9
