ライフサイエンスコーパス: transcriptomics

1 ene regulatory networks by using single-cell transcriptomics.

2 n mutagenesis forward genetic screening, and transcriptomics.

3 e cell imaging with high temporal resolution transcriptomics.

4 single-nucleus DNA methylation, and spatial transcriptomics.

5 iameter around amyloid plaques using spatial transcriptomics.

6 phenotypes that are difficult to infer from transcriptomics.

7 using single-cell RNA sequencing and spatial transcriptomics.

8 rate cell surface phenotyping to single-cell transcriptomics.

9 completed the entire grid of spatiotemporal transcriptomics.

10 assay, colony organizations are profiled by transcriptomics.

11 f human dermal fibroblasts using single-cell transcriptomics.

12 se to infection, particularly those based on transcriptomics.

13 reveal diversity not captured by single-cell transcriptomics.

14 od due to the limited sensitivity of in situ transcriptomics.

15 assessed using histology, biochemistry, and transcriptomics.

16 denocarcinoma and renal transplant rejection transcriptomics.

17 chnologies including imaging, proteomics and transcriptomics.

18 and erythropoiesis signatures by whole-blood transcriptomics.

19 , natural genetic variation, proteomics, and transcriptomics.

20 holistic approach ranging from microscopy to transcriptomics.

21 st for single cell cloning, phenotyping, and transcriptomics.

22 e cellular diversity revealed by single-cell transcriptomics.

23 ent subgroups through deconvolution of serum transcriptomics: 1) increased neutrophils and naive CD4

24 ce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for

25 Transcriptomics after mTORC1/2 inhibition confirmed decr

26 Spatial transcriptomics allows for the measurement of RNA abunda

27 WHERE NEXT?: Single-cell transcriptomics allows the fate of individual astrocytes

28 Complementing our metabolomics results, our transcriptomics analyses also revealed significant alter

29 Transcriptomics analyses identified a role for the MEK5-

30 WT mice are only mildly glucose intolerant, transcriptomics analyses reveal islets from HFD-fed Galp

31 RNA sequencing and transcriptomics analyses were performed and confirmed by

32 mic reticulum-were not reflected in previous transcriptomics analyses.

33 Transcriptomics analysis by RNA-sequencing reveals that

34 Unbiased transcriptomics analysis followed by Ingenuity Pathway A

35 Transcriptomics analysis has identified p120-catenin as

36 Many computational methods for transcriptomics analysis have been developed, evaluated

37 onal low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view.

38 uman brain, we have performed single-nucleus transcriptomics analysis of >110,000 neuronal transcript

39 E4's role in AD pathogenesis, we performed a transcriptomics analysis of APOE4 vs. APOE3 expression i

40 A transcriptomics analysis of Arabidopsis (Arabidopsis tha

41 g human HSC development, we combined spatial transcriptomics analysis of dorsoventral polarized signa

42 Single-cell transcriptomics analysis of GC B cells revealed that whe

43 Genome-wide spatial transcriptomics analysis provides an unprecedented appro

44 Transcriptomics analysis revealed a tight link between c

45 e (NH(2)OH) as intermediate, and comparative transcriptomics analysis revealed an alternative pathway

46 Transcriptomics analysis revealed downregulation of endo

47 H. pylori co-culture with global time-course transcriptomics analysis to identify new regulatory gene

48 Based on the results of transcriptomics analysis, we found 71 differentially exp

49 ulated Raman scattering (SRS) microscopy and transcriptomics analysis, we identify the fatty acid syn

50 Using comparative transcriptomics analysis, we present that HAR genes are

51 By single-cell transcriptomics and bioinformatics, both signaling and c

52 Using single-cell transcriptomics and chromatin accessibility, we gained a

53 Integrating metabolomics, transcriptomics and computational modeling, we identify

54 Here, we used single-cell transcriptomics and CyTOF-based single-cell protein quan

55 subgroups of critical illness based on serum transcriptomics and derived immune cell fractions, with

56 observations are corroborated by single cell transcriptomics and emphasize how NCoR1 and SMRT may con

57 Transcriptomics and epigenetic assessment of lupus LDGs,

58 al. use single-cell transcriptomics and epigenomics in mice and human sample

59 Single-cell transcriptomics and functional assays place fetal PrePro

60 Using a combination of transcriptomics and functional genomics, we unexpectedly

61 Our integrated transcriptomics and gene set enrichment analysis (GSEA)

62 Our integrated transcriptomics and gene set enrichment analysis studies

63 of technological advancements in single-cell transcriptomics and highlight some of the recent discove

64 29, 1030-1043] performed MRI, muscle biopsy transcriptomics and histopathology on a cohort of FSHD p

65 e-cell RNA sequencing, combined with spatial transcriptomics and immunohistochemistry, to comprehensi

66 Single-cell transcriptomics and immunostaining of both WT and DKO ER

67 Comparative transcriptomics and in silico analysis identified a smal

68 ughput and DR subdomain-targeted single-cell transcriptomics and intersectional genetic tools to map

69 multiple in vivo repair models, single-cell transcriptomics and lineage tracing, we find that alveol

70 High-resolution respirometry, transcriptomics and mass spectrometry establish that H(2

71 An integrated transcriptomics and metabolomics analysis reveals that A

72 Overall the transcriptomics and metabolomics data in our study expla

73 genetic studies and available information on transcriptomics and metabolomics.

74 ined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a

75 ies of seed maturation regulators, combining transcriptomics and network analysis, suggest the signif

76 Single-cell transcriptomics and other analyses strongly implicate el

77 treatment efficacy by integrating genomics, transcriptomics and protein profiling including modifica

78 Finally, the intersection of single-cell transcriptomics and proteomic analysis uncovers key huma

79 Unbiased transcriptomics and proteomics analysis on cytokine-prod

80 e integration and validation analysis of the transcriptomics and proteomics data revealed two common

81 Integrative data from transcriptomics and proteomics revealed MZB1 as a potent

82 cise as we gain more knowledge from applying transcriptomics and proteomics to blood and airway sampl

83 The data from transcriptomics and proteomics were integrated to reveal

84 ing of the virus to include intra-host viral transcriptomics and the characterization of host respons

85 Here, through a combination of transcriptomics and transgenesis, we identify sestrins,

86 This was consistent with bulk PBMC transcriptomics and transient, low IFN-alpha levels in p

87 of the claustrum, identified by single-cell transcriptomics and viral tracing of connectivity, also

88 eakthroughs in high-throughput technologies, transcriptomics, and advances in our understanding of ge

89 Next, in vivo two-photon imaging, transcriptomics, and computational modeling reveal that

90 We combined lineage tracing, single-cell transcriptomics, and electrophysiology of the mouse reti

91 asis on neuroendocrinology, neuroplasticity, transcriptomics, and epigenetics.

92 ch method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover

93 Thus, we employed genomics, transcriptomics, and functional approaches to reveal one

94 mass spectrometry-based shotgun proteomics, transcriptomics, and glycomics methods on 8 pediatric KD

95 tracing, proliferation kinetics, single-cell transcriptomics, and in vitro micro-pattern experiments,

96 rised by p53 profiling, exome sequencing and transcriptomics, and karyotyped using single-cell whole-

97 technological advances, including serology, transcriptomics, and metabolomics, have provided new ins

98 solution imaging, viral tracing, single-cell transcriptomics, and optogenetics, we identified and fun

99 everse genetics, quantitative N-terminomics, transcriptomics, and physiological assays to characteriz

100 V-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interacto

101 the complex relationships between genomics, transcriptomics, and proteomics requires the development

102 cing, RNA-Seq- and quantitative RT-PCR-based transcriptomics, and ultra-high-performance LC-MS/MS- an

103 en that bacteria influence host health, this transcriptomics approach can elucidate genes mediating h

104 In this study, we used a total transcriptomics approach to characterize and compare the

105 Using a time-course transcriptomics approach, global gene expression respons

106 , and interpretations of current single cell transcriptomics approaches from the perspective of emplo

107 logical experiments including proteomics and transcriptomics approaches often reveal sets of proteins

108 sum, combining MACS with immunochemical and transcriptomics approaches provides an ideal workflow to

109 Recent transcriptomics approaches to classify human CRC reveale

110 We used transcriptomics approaches to compare the scope and kine

111 titative trait locus mapping and comparative transcriptomics approaches.

112 ensors, advanced microscopy, and single-cell transcriptomics are advancing plant synthetic biology.

113 hen "omics" tools (genomics, proteomics, and transcriptomics) are applied to manipulated cell lines a

114 tion from other "-omics" (e.g., epigenomics, transcriptomics as measured by RNA expression) at both t

115 eomics, cross-referenced to unbiased cardiac transcriptomics at single-cell resolution, prioritized p

116 Through single-cell transcriptomics, ATAC-Seq and ChIP-Seq profiling, we exp

117 nsory neurons, we generated a LN single-cell transcriptomics atlas and nominated nociceptor target po

118 Transcriptomics-based phenotype prediction benefits from

119 Reliability and reproducibility of transcriptomics-based studies are dependent on RNA integ

120 Comparative transcriptomics between differentiating human pluripoten

121 Comparative transcriptomics between low- and high-acylsugar-producin

122 pressed genes were identified when comparing transcriptomics between subjects with CpcPH and those wi

123 Stage- and tissue-specific comparative transcriptomics between Zeo1.b and its parent cultivar s

124 ells at E3.25-HNC with over 3,800 genes with transcriptomics bifurcation, many of which are PE and EP

125 Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma

126 ist of high confidence (core) reactions from transcriptomics, but parameters related to identificatio

127 family and demonstrates how powerful de novo transcriptomics can be at accelerating the discovery of

128 Nasal transcriptomics can provide an accessible window into as

129 For the transcriptomics case, we demonstrate that the optimized

130 duces mechanical hypersensitivity and global transcriptomics changes in a sex-specific manner.

131 s by combining gnotobiotic mouse models with transcriptomics, circuit-tracing methods and functional

132 We employed single-cell transcriptomics combined with cell surface epitope detec

133 eep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boo

134 Transcriptomics confirms transposon-mediated effects on

135 Single-cell transcriptomics coupled with dynamic two-color fluoresce

136 s upon prior methods used to analyze spatial transcriptomics data and can propose novel pairs of extr

137 Application to the integrative analysis of transcriptomics data and proteomics data from a cancer s

138 ature, we performed network deconvolution of transcriptomics data derived from tissues possessing mot

139 antified by ELISA (n = 181) and examined via transcriptomics data from external cohorts.

140 Transcriptomics data from Lake Rotsee (Switzerland) show

141 Deconvolution of bulk transcriptomics data from mixed cell populations is vita

142 ations across space using spatially resolved transcriptomics data from the mouse olfactory bulb.

143 iently explore, analyze, and visualize their Transcriptomics data interactively.

144 Analysis of transcriptomics data showed distinctively low expression

145 ics approach of patient-derived genomics and transcriptomics data suggested only minimal effects on e

146 al data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dyn

147 Transcriptomics data were collected from the livers of S

148 3 BGCs were cross-referenced against public transcriptomics data, and were found to be highly expres

149 Additional studies, as well as the original transcriptomics data, suggest that multiple bioenergetic

150 ions between two cell types from single-cell transcriptomics data.

151 to facilitate cluster evaluation for spatial transcriptomics data.

152 sed to estimate chemical concentrations from transcriptomics data.

153 ster relationships when dealing with Spatial Transcriptomics data.

154 exponentially growing corpus of genome-wide transcriptomics data.

155 copy number variations (CNVs) not evident in transcriptomics data.

156 applications to untargeted metabolomics and transcriptomics data.

157 cer data from TCGA, the largest genomics and transcriptomics database, support our findings.

158 state cancer and other cancers where spatial transcriptomics datasets are available.

159 Here, we mine transcriptomics datasets to investigate signalling in th

160 uencing, chromatin accessibility and imaging transcriptomics datasets, and show that Augur outperform

161 To aid interpretation of large transcriptomics datasets, we also report a new method th

162 oteomics, genomics, metabolomics, models and transcriptomics datasets.

163 We report microbiota, host transcriptomics, epigenomics and genetics from matched i

164 h an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomi

165 application of -omics approaches, including transcriptomics, epigenomics, microbiomics, metabolomics

166 ious omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomic

167 ork to enable a comprehensive Functional Iso-Transcriptomics (FIT) analysis, which is effective at re

168 By integrating whole-blood transcriptomics, flow-cytometric analysis, and plasma cy

169 Single-cell transcriptomics from dissociated kidneys facilitated the

170 Single-cell transcriptomics from dissociated kidneys provided suffic

171 Furthermore, gp120-specific B cell transcriptomics from MVA-boosted and protein-boosted vac

172 ith publicly available data on toxicological transcriptomics from propranolol exposure, and with micr

173 Using bulk RNA sequencing or microarray transcriptomics from tissue samples (4 SB and 2 colonic

174 Transcriptomics, genome sequencing, and metabolomics ana

175 ll technologies, including the assessment of transcriptomics, genomics, and proteomics at the level o

176 gene at a time, we systematically integrated transcriptomics, genotypes and Hi-C data to identify int

177 Single-cell transcriptomics has been widely applied to classify neur

178 High-throughput transcriptomics has matured into a very well established

179 Single-cell transcriptomics has radically improved our ability to ch

180 echnologies, such as single-cell and spatial transcriptomics, has fostered sophisticated new methods

181 Spatial transcriptomics have demonstrated profound zonation of e

182 evelopments in the emerging field of spatial transcriptomics have opened up an unexplored landscape w

183 Studies involving transcriptomics have revealed multiple molecular subtype

184 Recently, transcriptomics have revealed that infection by O. elekt

185 ary technique to characterize inter-cellular transcriptomics heterogeneity.

186 er, our results demonstrate that single cell transcriptomics holds promise for studying plant develop

187 Supporting these findings, transcriptomics identified changes in genes involved in

188 Thus, oxidative stress transcriptomics identified neurotoxic CNS innate immune

189 Hydra by using a combination of single-cell transcriptomics, immunochemistry, and functional experim

190 sequencing) experiments in mice, and spatial transcriptomics in human kidney fibrosis, to shed light

191 identified novel myeloid subtypes, based on transcriptomics in single cells, that represent therapeu

192 Through the integration of transcriptomics, in situ hybridization and immunohistoch

193 he urine glycoproteins was regulated in IgAN transcriptomics, indicating that tissue remodelling rath

194 ve multiplex imaging, genetic perturbations, transcriptomics, infection-based assays and mathematical

195 Single-cell transcriptomics is enabling, for the first time, systema

196 Single cell transcriptomics is revolutionising our understanding of

197 ood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodie

198 Here we use a combination of transcriptomics, metabolic logic and pathway reconstitut

199 Combining genomics, transcriptomics, metabolomics, and biochemistry, we iden

200 Overlapping associations with transcriptomics, metabolomics, and clinical endpoints su

201 We combine a microarray-based spatial transcriptomics method that reveals spatial patterns of

202 Recent advances in transcriptomics, multiplex protein expression analysis,

203 Using single-cell transcriptomics, novel epithelial cell types are being u

204 combined chromatin profiling and single-cell transcriptomics of a conditional knockout mouse, we demo

205 glycolate metabolism, performing comparative transcriptomics of autotrophic growth under low and high

206 ioblastoma tumor cell atlas with single-cell transcriptomics of cancer cells mapped onto a reference

207 Transcriptomics of EP4 grown on 4-propylguaiacol (4PG) r

208 ation, morphophysiology, microcircuitry, and transcriptomics of mouse hippocampal CA1 parvalbumin-con

209 Single-cell transcriptomics of neocortical neurons have revealed mor

210 Transcriptomics of PSEN1-deficient cells reveals strongl

211 Metagenomics and transcriptomics of SJ3 reveal a diverse community compri

212 ciated with DMD, we performed single-nucleus transcriptomics of skeletal muscle of mice with dystroph

213 bserved inhibition, we performed single-cell transcriptomics on OSNs exhibiting specific response pro

214 We applied integrative transcriptomics on six varieties exhibiting different le

215 Here, we conducted comparative transcriptomics on soybean hairy roots of the variety Wi

216 Unfortunately, methods optimized for bulk transcriptomics perform poorly on scRNA-seq data and pro

217 However, transcriptomics performed on whole kidneys provides limi

218 pothesis and compare the two diseases from a transcriptomics perspective.

219 lishment of the first full-scale Associative Transcriptomics platform for B. juncea enables rapid pro

220 present the validation of a new Associative Transcriptomics platform in the important oilseed crop B

221 nces and structures, proteomics, single-cell transcriptomics, population-wide genetic association stu

222 Transcriptomics profiling revealed that multiple sensory

223 Using a combination of transcriptomics, protein expression, and functional anal

224 In contrast to transcriptomics, proteomics and metabolomics generate da

225 Our transcriptomics, proteomics and phospho-proteomics studi

226 insertion site sequencing (TraDIS), RNA-seq transcriptomics, proteomics and stable isotopic labellin

227 s especially pivotal for many neurogenomics, transcriptomics, proteomics, and connectomics studies, y

228 We integrated coincident transcriptomics, proteomics, and metabolomics data at se

229 n = 57 for the discovery cohort (clinical, transcriptomics, proteomics, and metabolomics data) and

230 High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integ

231 chnologies including genomics, metagenomics, transcriptomics, proteomics, and metabolomics.

232 utilized a multiomics approach (epigenomics, transcriptomics, proteomics, and phosphoproteomics) to c

233 OmicsDI aggregates genomics, transcriptomics, proteomics, metabolomics and multiomics

234 single nucleotide polymorphisms with AVNFH, transcriptomics, proteomics, metabolomics, biophysical,

235 R and mass spectrometry imaging, microscopy, transcriptomics, proteomics, metabolomics, lipidomics, i

236 Whole blood transcriptomics provides a unique opportunity to follow

237 ughput of genetic screening with single-cell transcriptomics readout.

238 of the peer review process for "Single-Cell Transcriptomics Resolves Intermediate Glial Progenitors

239 We present a hiPSC transcriptomics resource on corticogenesis from 5 iPSC d

240 Cs samples were analyzed by metabolomics and transcriptomics, respectively.

241 We hypothesize that selected transcriptomics responses, particularly immune mechanism

242 Single-cell transcriptomics revealed a lack of type I IFNs, reduced

243 gle-cell atlas of the kidney generated using transcriptomics revealed marked changes in the proportio

244 Cerebellar interpositus transcriptomics revealed substantial sex effects, with m

245 Analysis of myeloid cell transcriptomics revealed that ADAMTS5 is enriched in hum

246 Comparative transcriptomics revealed the enrichment of biological pr

247 worm taxis, we employ comparative genomics, transcriptomics, reverse genetics, and chemical approach

248 whole exome sequencing, bulk and single-cell transcriptomics, ribosome profiling, and two MS/MS searc

249 d analyzed by high-throughput proteomics and transcriptomics (RNA-seq).

250 term memory formation using a combination of transcriptomics, RNA-binding protein immunoprecipitation

251 Spatial transcriptomics seeks to integrate single cell transcrip

252 Unbiased transcriptomics shows an upregulation of collagens in bo

253 cular and imaging tools, such as single-cell transcriptomics, single-molecule fluorescence in situ hy

254 cular and clinical heterogeneity of SLE from transcriptomics studies and detail their potential impac

255 for large-scale and cost-effective bacterial transcriptomics studies.

256 mbination of electron microscopy imaging and transcriptomics study reveals an unexpected 2-step proce

257 for TNFSF2 (TNF-alpha) cannot be ruled out, transcriptomics suggest that maintenance of O-MALT in no

258 Raman spectro-microscopy with lipidomics and transcriptomics suggests possible lipid regulatory mecha

259 Single cell transcriptomics technologies have vast potential in adva

260 Single-cell transcriptomics, the ability to track individual T cell

261 The application of transcriptomics to a wild avian species, naturally expos

262 Here, we use single-cell transcriptomics to chart the cellular landscape of upper

263 We used RNA-Seq-based high-resolution transcriptomics to compare gene expression in activated

264 Here we used single-nucleus transcriptomics to examine ~80,000 nuclei from the dorso

265 Here, we use single cell transcriptomics to generate a high-resolution cell state

266 We used single-cell transcriptomics to generate an unbiased classification o

267 predictions as well as short- and long-read transcriptomics to generate highly complete gene annotat

268 ve applied transgenics, lineage-tracing, and transcriptomics to help decipher the distinct roles of t

269 omics, sensitivity correlation analysis, and transcriptomics to identify a common MoA for the antican

270 StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to buil

271 , we integrated chromatin accessibility with transcriptomics to identify putative enhancer-gene linka

272 Here we used single-cell transcriptomics to investigate intra- and intertumoral h

273 We applied single-cell transcriptomics to map the heterogeneity of sinusoid-ass

274 We used single-cell transcriptomics to profile 32,000 STMs and identified ph

275 hPSC) differentiation system and single-cell transcriptomics to recapitulate EHT in vitro and uncover

276 o redress this we used tandem MS and de novo transcriptomics to rediscover evolidine and decipher its

277 an anteroposterior axis, and we use spatial transcriptomics to show that they exhibit patterned gene

278 e combine single-cell and spatially resolved transcriptomics to systematically map the molecular, cel

279 mbine cell lineage barcoding and single-cell transcriptomics to trace the emergence of drug resistanc

280 nes retrograde viral tracing and single-cell transcriptomics to uncover the molecular identities of u

281 atycarpus were obtained based on comparative transcriptomics under Helicoverpa armigera infestation.

282 kers, whole-cell recordings, and single-cell transcriptomics validated these findings in a functional

283 High resolution transcriptomics was used to decipher EV-A71-host interac

284 Leveraging high-throughput transcriptomics we identify NMD targets transcriptome-wi

285 Using RNA-sequencing transcriptomics we investigated lung gene expression res

286 hromatin accessibility data with single-cell transcriptomics, we find that NPCs place an early priori

287 Using transcriptomics, we found that components of the Fibrobl

288 Using single cell transcriptomics, we identified a subpopulation of Dictyo

289 Using single-cell transcriptomics, we identified a tissue-specific core si

290 Here, using single cell transcriptomics, we identify a specific location-associa

291 Integrating metabolomics, lipidomics, and transcriptomics, we link changes in the lipidome of prol

292 a combination of whole-genome sequencing and transcriptomics, we showed that tolerance could be attri

293 Using single-cell transcriptomics, we systematically classified RGCs in ad

294 Plasma IL-6 and leukocyte transcriptomics were better predictors of outcomes than

295 Here we combine transcriptomics with detailed growth analysis to identif

296 Coupling transcriptomics with genetics, we show that emerging hai

297 Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs

298 We have integrated transcriptomics with histomorphological scores, identifi

299 nd ulcerative colitis (UC) using single-cell transcriptomics with T-cell receptor repertoire analysis

300 e gene repertoire sequencing and single-cell transcriptomics yielded direct evidence of a parallel bo