title: Single-cell transcriptome analysis of endometrial tissue.
abstract: OBJECTIVE
  How can we study the full transcriptome of endometrial stromal and epithelial cells at the single-cell level?
  
  
  CONCLUSIONS
  By compiling and developing novel analytical tools for biopsy, tissue cryopreservation and disaggregation, single-cell sorting, library preparation, RNA sequencing (RNA-seq) and statistical data analysis.
  
  
  BACKGROUND
  Although single-cell transcriptome analyses from various biopsied tissues have been published recently, corresponding protocols for human endometrium have not been described.
  
  
  METHODS
  The frozen-thawed endometrial biopsies were fluorescence-activated cell sorted (FACS) to distinguish CD13-positive stromal and CD9-positive epithelial cells and single-cell transcriptome analysis performed from biopsied tissues without culturing the cells. We studied gene transcription, applying a modern and efficient RNA-seq protocol. In parallel, endometrial stromal cells were cultured and global expression profiles were compared with uncultured cells.
  
  
  METHODS
  For method validation, we used two endometrial biopsies, one from mid-secretory phase (Day 21, LH+8) and another from late-secretory phase (Day 25). The samples underwent single-cell FACS sorting, single-cell RNA-seq library preparation and Illumina sequencing.
  
  
  RESULTS
  Here we present a complete pipeline for single-cell gene-expression studies, from clinical sampling to statistical data analysis. Tissue manipulation, starting from disaggregation and cell-type-specific labelling and ending with single-cell automated sorting, is managed within 90 min at low temperature to minimize changes in the gene expression profile. The single living stromal and epithelial cells were sorted using CD13- and CD9-specific antibodies, respectively. Of the 8622 detected genes, 2661 were more active in cultured stromal cells than in biopsy cells. In the comparison of biopsy versus cultured cells, 5603 commonly expressed genes were detected, with 241 significantly differentially expressed genes. Of these, 231 genes were up- and 10 down-regulated in cultured cells, respectively. In addition, we performed a gene ontology analysis of the differentially expressed genes and found that these genes are mainly related to cell cycle, translational processes and metabolism.
  
  
  CONCLUSIONS
  Although CD9-positive single epithelial cells sorting was successfully established in our laboratory, the amount of transcriptome data per individual epithelial cell was low, complicating further analysis. This step most likely failed due to the high dose of RNases that are released by the cells' natural processes, or due to rapid turnaround time or the apoptotic conditions in freezing- or single-cell solutions. Since only the cells from the late-secretory phase were subject to more focused analysis, further studies including larger sample size from the different time-points of the natural menstrual cycle are needed. The methodology also needs further optimization to examine different cell types at high quality.
  
  
  CONCLUSIONS
  The symbiosis between clinical biopsy and the sophisticated laboratory and bioinformatic protocols described here brings together clinical diagnostic needs and modern laboratory and bioinformatic solutions, enabling us to implement a precise analytical toolbox for studying the endometrial tissue even at the single-cell level.
authors.affiliation: Competence Centre on Health Technologies, Tartu 50410, Estonia Department of Biosciences and Nutrition, and Center for Innovative Medicine, Karolinska Institutet, Huddinge 141 83, Sweden kaarel.krjutshkov@ki.se shintaro.katayama@ki.se.
authors.forename: K
authors.initials: K
authors.lastname: Krjutškov
authors.affiliation: Department of Biosciences and Nutrition, and Center for Innovative Medicine, Karolinska Institutet, Huddinge 141 83, Sweden kaarel.krjutshkov@ki.se shintaro.katayama@ki.se.
authors.forename: S
authors.initials: S
authors.lastname: Katayama
authors.affiliation: Competence Centre on Health Technologies, Tartu 50410, Estonia Department of Obstetrics and Gynaecology, University of Tartu, Tartu 51014, Estonia.
authors.forename: M
authors.initials: M
authors.lastname: Saare
authors.affiliation: Igenomix, Valencia 46980, Spain.
authors.forename: M
authors.initials: M
authors.lastname: Vera-Rodriguez
authors.affiliation: Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia.
authors.forename: D
authors.initials: D
authors.lastname: Lubenets
authors.affiliation: Competence Centre on Health Technologies, Tartu 50410, Estonia.
authors.forename: K
authors.initials: K
authors.lastname: Samuel
authors.affiliation: Competence Centre on Health Technologies, Tartu 50410, Estonia Department of Obstetrics and Gynaecology, University of Tartu, Tartu 51014, Estonia.
authors.forename: T
authors.initials: T
authors.lastname: Laisk-Podar
authors.affiliation: Competence Centre on Health Technologies, Tartu 50410, Estonia.
authors.forename: H
authors.initials: H
authors.lastname: Teder
authors.affiliation: Department of Biosciences and Nutrition, and Center for Innovative Medicine, Karolinska Institutet, Huddinge 141 83, Sweden Molecular Neurology Research Program, University of Helsinki and Folkhälsan Institute of Genetics, Helsinki 00014, Finland.
authors.forename: E
authors.initials: E
authors.lastname: Einarsdottir
authors.affiliation: Competence Centre on Health Technologies, Tartu 50410, Estonia Department of Obstetrics and Gynaecology, University of Tartu, Tartu 51014, Estonia Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu 50411, Estonia.
authors.forename: A
authors.initials: A
authors.lastname: Salumets
authors.affiliation: Department of Biosciences and Nutrition, and Center for Innovative Medicine, Karolinska Institutet, Huddinge 141 83, Sweden Molecular Neurology Research Program, University of Helsinki and Folkhälsan Institute of Genetics, Helsinki 00014, Finland.
authors.forename: J
authors.initials: J
authors.lastname: Kere
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000378:metabolism; D018826:CD13 Antigens / Q000378:metabolism; D002469:Cell Separation; D002478:Cells, Cultured; D015925:Cryopreservation; D004717:Endometrium / Q000166:cytology / Q000378:metabolism*; D004847:Epithelial Cells / Q000166:cytology / Q000378:metabolism; D004957:Estonia; D005260:Female; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D015723:Gene Library; D063990:Gene Ontology; D006801:Humans; D008183:Luteal Phase; D012333:RNA, Messenger / Q000737:chemistry / Q000378:metabolism*; D017423:Sequence Analysis, RNA; D059010:Single-Cell Analysis; D017154:Stromal Cells / Q000166:cytology / Q000378:metabolism; D060245:Tetraspanin 29 / Q000378:metabolism; D059467:Transcriptome
chemical_list: D015415:Biomarkers; C554869:CD9 protein, human; D012333:RNA, Messenger; D060245:Tetraspanin 29; D018826:CD13 Antigens
keywords: biopsy cryopreservation; clinical sampling; endometrial biopsy; endometrial receptivity; single-cell FACS