{"ok":true,"nota":{"id":"36982dc5741a1781","slug":"un-metodo-nuevo-identifica-en-que-momento-del-dia-esta-cada-celula-36982d","titulo":"Un método nuevo identifica en qué momento del día está cada célula","resumen":"Los investigadores presentaron scRhythmBayes, un algoritmo probabilístico que estima la fase circadiana de cada célula individual a partir de datos de transcriptómica de célula única. El método modela cómo esa información parcial afecta los parámetros rítmicos de cada gen.","fuente":"biorxiv","fuenteNombre":"bioRxiv · preprints","color":"#be2628","autor":"Zhang, Q., Li, C., Deligiannis, I. K., Jiao, W., Liu, W., Martinez-Jimenez, C. P., Wang, Z., A. Dyar, K., Zhang, L., Sun","url":"https://www.biorxiv.org/content/10.64898/2026.10.18.759931v1?rss=1","imagen":"/hoy/portada/36982dc5741a1781","tema":"ciencia","subtemas":["ritmos circadianos","transcriptomica de celula unica","bioinformatica","cronobiologia","estadistica bayesiana"],"entidades":["bioRxiv","scRhythmBayes"],"formato":"dato","entretenimiento":false,"pubTs":"2026-10-22T00:00:00.000Z","indexTs":"2026-10-23T06:48:38.804Z","hash":"aa7679f63e1b9c317d312292c004cb5d816737c028642ea5e3658c7e378761ec","tsa":{"time":"Oct 23 06:48:59 2026 GMT","provider":"freetsa.org","sobre":"sello"},"stats":{"views":0,"likes":0,"clicks":0,"dwell":0},"tipo":"normal","registro":"hallazgo","porQueImporta":"Muchas funciones del cuerpo, como el sueño, el metabolismo o la respuesta inmune, siguen ritmos de 24 horas que varían entre tipos de células. Poder leer ese reloj célula por célula permite entender mejor cuándo conviene tomar un medicamento o cuándo un tejido está más vulnerable. Por ahora es una herramienta para investigadores y no hay aplicación directa para pacientes. Todavía falta validarla en más tejidos y condiciones.","quienFirma":"Preprint en bioRxiv (institución principal no identificada en el resumen)","confianza":"preprint","consulta":null},"sello":{"hash":"aa7679f63e1b9c317d312292c004cb5d816737c028642ea5e3658c7e378761ec","sello":"bdf216757102f04c0819361bd7ef22aa24fddf146138b3e718d31327bfedf92a","beacon":{"drand":{"cadena":"8990e7a9aaed2ffed73dbd7092123d6f289930540d7651336225dc172e51b2ce","ronda":6576903,"aleatorio":"8e583525a288ba13cde66d0d6539d282d867cec58f75a0217a855687a27f9649","tiempo":"2026-10-23T06:48:30.000Z"},"nist":{"cadena":2,"pulso":1995651,"salida":"AA82134D649B7B25F10D77FD67053808011261EF94B49E048C931DF7D357E824C95C66604E50CE6E37F934A39EE493177DEA66983586EA3E4667145C3C8919D6","tiempo":"2026-10-23T06:48:00.000Z"},"noAntes":"2026-10-23T06:48:30.000Z"},"verificacion":{"drand":true,"nist":true},"tsa":{"time":"Oct 23 06:48:59 2026 GMT","provider":"freetsa.org","sobre":"sello"},"original":{"titulo":"Decoding Cell-level Circadian Phase and Rhythm-state Remodeling in Single-cell Transcriptomes With scRhythmBayes","descripcion":"Current circadian omics analysis tools have been developed primarily for bulk time-series. However, heterogeneous single-cell datasets require analysis and interpretation of rhythmic programs across different cells, cell types, and biological conditions. In these datasets, cellular circadian phase is usually unobserved, and sample-level zeitgeber time provides temporal information but does not define the exact phase of every cell. Existing methods address only selected aspects of this problem and do not model how cell-level phase evidence informs gene-level rhythmic parameters and rhythm-state remodeling. Here we present scRhythmBayes, a new probabilistic algorithm for phase-resolved circadi","pubDate":"2026-10-22","url":"https://www.biorxiv.org/content/10.64898/2026.10.18.759931v1?rss=1"},"indexado":"2026-10-23T06:48:38.804Z"}}