Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos
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eLife Assessment
This important study reports the transcriptomic and proteomic landscape of the oviducts at four different preimplantation periods during natural fertilization, pseudopregnancy, and superovulation. The data presented convincingly supported the conclusion in general, although more analyses would strengthen the conclusions drawn. This work will interest reproductive biologists and clinicians practicing reproductive medicine.
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Abstract
The oviduct is the site of fertilization and preimplantation embryo development in mammals. Evidence suggests that gametes alter oviductal gene expression. To delineate the adaptive interactions between the oviduct and gamete/embryo, we performed a multi-omics characterization of oviductal tissues utilizing bulk RNA-sequencing (RNA-seq), single-cell RNA-sequencing (scRNA-seq), and proteomics collected from distal and proximal at various stages after mating in mice. We observed robust region-specific transcriptional signatures. Specifically, the presence of sperm induces genes involved in pro-inflammatory responses in the proximal region at 0.5 days post-coitus (dpc). Genes involved in inflammatory responses were produced specifically by secretory epithelial cells in the oviduct. At 1.5 and 2.5 dpc, genes involved in pyruvate and glycolysis were enriched in the proximal region, potentially providing metabolic support for developing embryos. Abundant proteins in the oviductal fluid were differentially observed between naturally fertilized and superovulated samples. RNA-seq data were used to identify transcription factors predicted to influence protein abundance in the proteomic data via a novel machine learning model based on transformers of integrating transcriptomics and proteomics data. The transformers identified influential transcription factors and correlated predictive protein expressions in alignment with the in vivo -derived data. In conclusion, our multi-omics characterization and subsequent in vivo confirmation of proteins/RNAs indicate that the oviduct is adaptive and responsive to the presence of sperm and embryos in a spatiotemporal manner.
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eLife Assessment
This important study reports the transcriptomic and proteomic landscape of the oviducts at four different preimplantation periods during natural fertilization, pseudopregnancy, and superovulation. The data presented convincingly supported the conclusion in general, although more analyses would strengthen the conclusions drawn. This work will interest reproductive biologists and clinicians practicing reproductive medicine.
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Reviewer #1 (Public review):
Summary:
The paper demonstrated through a comprehensive multi-omics study of the oviduct that the transcriptomic and proteomic landscape of the oviduct at 4 different preimplantation periods was dynamic during natural fertilization, pseudopregnancy, and superovulation using three independent cell/tissue isolation and analytical techniques. This work is very important for understanding oviductal biology and physiology. In addition, the authors have made all the results available in a web search format, which will maximize the public's access and foster and accelerate research in the field.
Strengths:
(1) The manuscript addresses an important and interesting question in the field of reproduction: how does the oviduct at different regions adapt to the sperm and embryos for facilitating fertilization and …
Reviewer #1 (Public review):
Summary:
The paper demonstrated through a comprehensive multi-omics study of the oviduct that the transcriptomic and proteomic landscape of the oviduct at 4 different preimplantation periods was dynamic during natural fertilization, pseudopregnancy, and superovulation using three independent cell/tissue isolation and analytical techniques. This work is very important for understanding oviductal biology and physiology. In addition, the authors have made all the results available in a web search format, which will maximize the public's access and foster and accelerate research in the field.
Strengths:
(1) The manuscript addresses an important and interesting question in the field of reproduction: how does the oviduct at different regions adapt to the sperm and embryos for facilitating fertilization and preimplantation embryo development and transport?
(2) Authors used cutting-edge techniques: Integrated multi-modal datasets followed by in vivo confirmation and machine learning prediction.
(3) RNA-seq, scRNA-seq, and proteomic results are immediately available to the scientific community in a web search format.
(4) Substantiated results indicate the source of inflammatory responses was the secretory cell population in the IU region when compared to other cell types; sperm modulate inflammatory responses in the oviduct; the oviduct displays immuno-dynamism.
Weaknesses:
(1) The rationale for using the superovulation model is not clear. The oviductal response to sperm and embryos can be studied by comparing mating with normal and vasectomized mice and comparing pregnancy vs pseudopregnancy (induced by mating with vasectomized males). Superovulation causes supraphysiological hormone levels and other confounding conditions.
(2) This study involves a very complex dataset with three different models at four time points. If possible, it would be very informative to generate a graphic abstract/summary of their major findings in oviductal responses in different models and time points
(3) The resolution of Figures 3A-3C in the submitted file was not high enough to assess the authors' conclusion.
(4) The authors need to double-check influential transcription factors identified by machine learning. Apparently, some of them (such as Anxa2, Ift88, Ccdc40) are not transcription factors at all.
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Reviewer #2 (Public review):
The manuscript investigates oviductal responses to the presence of gametes and embryos using a multi-omics and machine learning-based approach. By applying RNA sequencing (RNA-seq), single-cell RNA sequencing (sc-RNA-seq), and proteomics, the authors identified distinct molecular signatures in different regions of the oviduct, proximal versus distal. The study revealed that sperm presence triggers an inflammatory response in the proximal oviduct, while embryo presence activates metabolic genes essential for providing nutrients to the developing embryos. Overall, this study offers valuable insights and is likely to be of great interest to reproductive biologists and researchers in the field of oviduct biology. However, further investigation into the impact of sperm on the immune cell population in the oviduct …
Reviewer #2 (Public review):
The manuscript investigates oviductal responses to the presence of gametes and embryos using a multi-omics and machine learning-based approach. By applying RNA sequencing (RNA-seq), single-cell RNA sequencing (sc-RNA-seq), and proteomics, the authors identified distinct molecular signatures in different regions of the oviduct, proximal versus distal. The study revealed that sperm presence triggers an inflammatory response in the proximal oviduct, while embryo presence activates metabolic genes essential for providing nutrients to the developing embryos. Overall, this study offers valuable insights and is likely to be of great interest to reproductive biologists and researchers in the field of oviduct biology. However, further investigation into the impact of sperm on the immune cell population in the oviduct is necessary to strengthen the overall findings.
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Author response:
eLife Assessment
This important study reports the transcriptomic and proteomic landscape of the oviducts at four different preimplantation periods during natural fertilization, pseudopregnancy, and superovulation. The data presented convincingly supported the conclusion in general, although more analyses would strengthen the conclusions drawn. This work will interest reproductive biologists and clinicians practicing reproductive medicine.
We appreciate the concise summary and agree that additional experiments can reinforce the fidelity of predictions made by our robust bioinformatic characterization of the oviduct. Our robust bioinformatic model appears reproducible as similar pathway trends have been produced in all three datasets, lending confidence for future researchers to establish testable hypotheses more …
Author response:
eLife Assessment
This important study reports the transcriptomic and proteomic landscape of the oviducts at four different preimplantation periods during natural fertilization, pseudopregnancy, and superovulation. The data presented convincingly supported the conclusion in general, although more analyses would strengthen the conclusions drawn. This work will interest reproductive biologists and clinicians practicing reproductive medicine.
We appreciate the concise summary and agree that additional experiments can reinforce the fidelity of predictions made by our robust bioinformatic characterization of the oviduct. Our robust bioinformatic model appears reproducible as similar pathway trends have been produced in all three datasets, lending confidence for future researchers to establish testable hypotheses more effectively.
Reviewer #1 (Public review):
The paper demonstrated through a comprehensive multi-omics study of the oviduct that the transcriptomic and proteomic landscape of the oviduct at 4 different preimplantation periods was dynamic during natural fertilization, pseudopregnancy, and superovulation using three independent cell/tissue isolation and analytical techniques. This work is very important for understanding oviductal biology and physiology. In addition, the authors have made all the results available in a web search format, which will maximize the public's access and foster and accelerate research in the field.
Strengths:
(1) The manuscript addresses an important and interesting question in the field of reproduction:
how does the oviduct at different regions adapt to the sperm and embryos for facilitating fertilization and preimplantation embryo development and transport?
(2) Authors used cutting-edge techniques: Integrated multi-modal datasets followed by in vivo confirmation and machine learning prediction.
(3) RNA-seq, scRNA-seq, and proteomic results are immediately available to the scientific community in a web search format.
(4) Substantiated results indicate the source of inflammatory responses was the secretory cell population in the IU region when compared to other cell types; sperm modulate inflammatory responses in the oviduct; the oviduct displays immuno-dynamism.
We sincerely thank you for your thorough and insightful review of our manuscript. Your comprehensive summary accurately captures the essence of our multi-omics study on oviductal biology, highlighting its importance in understanding reproductive physiology. We are particularly grateful for your recognition of our study's strengths. In the revised manuscript, we
plan to add another searchable scRNA-seq data on our public website; https://genesearch.org/winuthayanon/Oviduct_pregnancy/. We will also address the weaknesses in the response below in our revised manuscript.
Weaknesses:
(1) The rationale for using the superovulation model is not clear. The oviductal response to sperm and embryos can be studied by comparing mating with normal and vasectomized mice and comparing pregnancy vs pseudopregnancy (induced by mating with vasectomized males). Superovulation causes supraphysiological hormone levels and other confounding conditions.
We agree with this assessment that superovulation changes the hormonal levels and could have a confounding impact on the oviduct function. As such, for all experiments involving pseudopregnant datasets, pseudopregnancy was induced by mating females with vasectomized males without superovulation. In our oviductal luminal protein content analysis, oviductal fluid was collected from pregnant females with and without superovulation. This allowed us to directly compare the impact of superovulation on protein abundance and profile. In the revised manuscript, we will provide clarifying statements on using superovulation in our experimental design.
One exception for using superovulation in the absence of a “natural mating” group for comparison is the scRNA-seq dataset. As single-cell libraries should be performed in a single run to avoid batch effects, we need to ensure that the sufficient number of females were pregnant for single-cell isolation (we used ~4 mice/timepoint). Therefore, superovulation was used to synchronize and ensure that the females were receptive to mating. At the time of our sample collection, single nuclei isolation methods (freeze tissue now, isolate nuclei later) have not been reliable or standardized. We have tried to synchronize females using the male bedding without having to superovulate. However, we would still need to set up at least 12-15 females per pregnancy timepoint to mate with male mice, which totals to ~48-60 mice each night. Due to budget and vivarium space limitations, we were not able to do so. We will include a similar statement to explain and clarify these limitations in the revised manuscript.
(2) This study involves a very complex dataset with three different models at four time points. If possible, it would be very informative to generate a graphic abstract/summary of their major findings in oviductal responses in different models and time points
Thank you for this suggestion. We will include the graphical abstract to accompany our final version of the manuscript.
(3) The resolution of Figures 3A-3C in the submitted file was not high enough to assess the authors' conclusion.
We plan to provide a higher magnification of images in Figures 3A-C in the revised version.
(4) The authors need to double-check influential transcription factors identified by machine learning. Apparently, some of them (such as Anxa2, Ift88, Ccdc40) are not transcription factors at all.
We appreciate the recognition of this oversight. We will clearly state the distinction between ‘influential TFs’ and ‘significant proteins’ in the revised manuscript. We will ensure that all TFs are stated correctly.
Reviewer #2 (Public review):
The manuscript investigates oviductal responses to the presence of gametes and embryos using a multi-omics and machine learning-based approach. By applying RNA sequencing (RNAseq), single-cell RNA sequencing (sc-RNA-seq), and proteomics, the authors identified distinct molecular signatures in different regions of the oviduct, proximal versus distal. The study revealed that sperm presence triggers an inflammatory response in the proximal oviduct, while embryo presence activates metabolic genes essential for providing nutrients to the developing embryos. Overall, this study offers valuable insights and is likely to be of great interest to reproductive biologists and researchers in the field of oviduct biology. However, further investigation into the impact of sperm on the immune cell population in the oviduct is necessary to strengthen the overall findings.
We appreciate the concise summary, strengths, and weakness highlighted. We plan to address comments made by the reviewer concerning superovulation, figure recommendations, and additional analysis in our revised manuscript. We plan to include the comparison of findings from scRNA-seq analysis from fallopian tube tissues collected from hydrosalpinx patients by Ulrich et al. (PMID: 35320732) with our data. The evaluation of this data by Ulrich et al. will help distinguish between different inflammatory pathways stimulated by sperm vs. general inflammation. We will follow up on a detailed description of immune cell types present at 0.5 dpc using FACS analysis in future studies. This is mainly due to a lack of expertise and technical limitations in our lab on immune cell investigation. Nevertheless, we have made collaborative efforts and recruited two immunologists to facilitate our future immune cell studies. We will also provide a clear justification for using superovulation, especially in the scRNA-seq analysis in the revised manuscript (please see response to Reviewer 1 above).
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