{"doi":"10.1016/j.xpro.2024.103540","title":"Protocol for capturing a full transcriptome from single preimplantation embryos using So-Smart-seq","abstract":"Strand-optimized Smart-seq (So-Smart-seq) can capture a comprehensive transcriptome from low-input samples. This technique detects both polyadenylated and non-polyadenylated RNAs, inclusive of repetitive RNAs, while excluding highly abundant ribosomal RNAs. So-Smart-seq preserves strand information and minimizes 5′ to 3′ coverage bias. We describe steps for the analysis of single mouse preimplantation embryos, including embryo isolation, library preparation, ribosomal cDNA depletion, and initial data processing. The protocol may be adapted for other low-input samples and the detection of small RNAs of <200 nt. For complete details on the use and execution of this protocol, please refer to Wei et al. 1 • Collection of preimplantation embryos from the oviduct and uterus of female mice • Capture of full transcriptome in a single preimplantation embryo by So-Smart-seq • Preparation of oligo probes to deplete ribosomal cDNAs from libraries • Pre-processing of raw sequencing data for downstream analyses Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Strand-optimized Smart-seq (So-Smart-seq) can capture a comprehensive transcriptome from low-input samples. This technique detects both polyadenylated and non-polyadenylated RNAs, inclusive of repetitive RNAs, while excluding highly abundant ribosomal RNAs. So-Smart-seq preserves strand information and minimizes 5′ to 3′ coverage bias. We describe steps for the analysis of single mouse preimplantation embryos, including embryo isolation, library preparation, ribosomal cDNA depletion, and initial data processing. The protocol may be adapted for other low-input samples and the detection of small RNAs of <200 nt.","journal":"STAR Protocols","year":2025,"id":551050,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9452,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":208747,"name":"Jeannie T. Lee","orcid":"0000-0001-7786-8850","position":1,"is_corresponding":false},{"id":708920,"name":"Chunyao Wei","orcid":"0000-0003-3412-3407","position":0,"is_corresponding":true}],"reference_count":7,"raw_metadata":null,"created_at":"2026-07-19T02:54:20.915388Z","pmid":"39756032","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}