BioTuring studies
Search available studies in BioTuring single-cell database using standardized metadata
tissue
type of tissue or organ from which a sample is collected
adipose
adrenal gland
ascites
biliary system
bladder
blood
blood vessel
bone marrow
brain
breast
chest wall
embryonic tissue
esophagus
eye
heart
in vitro cell line
in vitro organoid
intestine
kidney
lymph node
mass
milk
muscle
oral cavity
peritoneum
reproductive system
respiratory system
skeletal system
skin
spinal cord
spleen
stomach
thymus
xenogratf
condition
medical condition of a donor
azoospermia
breast cancer
chronic inflammation
dementia due to Lewy body disease
diabetes
disease of the blood or blood-forming organs
disease of the circulatory system
disease of the digestive system
disease of the genitourinary system
disease of the immune system
disease of the musculoskeletal system or connective tissue
disease of the skin
disease of the visual system
fibrosis disease
infectious or parasitic disease
multiple diseases
neoplasm of brain or central nervous system
neoplasm of epithelial tissue
neoplasm of hematopoietic tissue
neoplasm of lymphoid tissues
neoplasm of plasma cell
neoplasm of supportive and connective tissues
nervous system disease
normal
nutritional or metabolic disease
obstructive sleep apnea
other neoplasms
presbyosmia
transplant rejection
unexplained repeated pregnancy loss
sampling site
where a sample is collected from a subject
adjacent normal tissue
blood
inflammatory site
lesion
metastatic site
primary tumor
cell type
authors' annotation about different cell types in a sample
B cell
blood cell
connective tissue cell
embryonic cell
endothelial cell
epithelial cell
extraembryonic cell
germ line cell
glial cell
innate lymphoid cell
kidney epithelial cell
muscle cell
myeloid leukocyte
neural cell
progenitor cell
retinal cell
secretory cell
somatic stem cell
T cell
cancer cell
malignant cells identified from a cancerous sample
central nervous system origin
epithelial origin
hematopoietic tissue origin
lymphoid tissue origin
other cancer types
plasma cell origin
undefined origin
treatment
type of therapy applied to a subject
androgen deprivation therapy
anti-inflammatory agent
anti-PD-1 and anti-CTLA-4 monoclonal antibody
antibiotics
antimetabolite
antiviral agent
CAR-T
chemotherapy
combined therapy
diabetes medication
disease-modifying antirheumatic drug
disease-modifying treatments
erythropoiesis–stimulating agent
histone deacetylase inhibitor
hormone
immunosuppressant medication
immunotherapy
monoclonal antibody
no treatment
proteasome inhibitor
protein kinase inhibitor
radiotherapy
steroid
T cell receptor engineering
vaccine
response to treatment
how a subject response after a course of treatment
clonal expansion
nonresponse
objective response
gender
biological gender of a donor
female
male
transfemale
developmental stage
age period of a donor
adolescent
adult
child
first trimester fetal
infant
second trimester fetal
third trimester fetal
sequencing platform
platform used for single-cell RNA-sequencing
10x
10X, single nuclei
automated microwell array-based platform
BD Rhapsody
C1 Fluidigm
CapID
CEL-Seq
CEL-Seq2
CITE-seq
ddSEQ
DroNc-seq
DropSeq
DropSeq, single nuclei
ECCITE-seq
GEXSCOPE scRNA
gmcSCRB-seq
ICELL8
inDrop
INs-seq
MARS-Seq
MARS-seq2.0
mCEL-Seq2
Microwell scRNA-seq
MIRACL-Seq
MutaSeq
Quartz-seq
scChaRM-seq
sci-Plex
sci-RNA-seq3
scTrio-seq2
Seq-Well
smart-seq
smart-seq2
SORT-seq
SPLiT-seq
STRT-seq
TARGET-seq
TruSeq
quantification
a program or package used for quantification of read counts
BEDTools
bioconductor package
celescope
CellRanger
Cufflinks
dropEst
Dropseq-tools
featureCount
GSNAP
HTSeq
HTSeqGenie
Immcantation framework
kallisto
MetaCell
Rhapsody analysis pipeline
rpkmforgenes
RSEM
RUM
Salmon
scopetools
SEQC
SPLiT-seq pipeline
STAR
UMI tools
USeq package
zUMIs
sampling technique
how a sample is collected
aspirate
biopsy
bronchial lavage
bronchoalveolar lavage
bronchoscopic microsampling
brushing
core needle biopsy
endoscopic biopsy
forceps biopsy
leukapheresis
lumbar puncture
nasopharyngeal swab
needle biopsy
paracentesis
punch biopsy
sputum induction
suction blister
surgical resection
thoracentesis
venipuncture
storage technique
how a sample is preserved before being processed
FFPE
fresh
fresh frozen
incubation
post-mortem analysis, fresh
post-mortem analysis, fresh frozen
1,013 studies
54,974,347 cells
Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy (Enriched glioblastoma stem cells)
ViewID:
couturier_et_al_2020_gsc
Total cells:
29,984
Number of matched cells:
29984
Cancer stem cells are critical for cancer initiation, development, and treatment resistance. Our understanding of these processes, and how they relate to glioblastoma heterogeneity, is limited. To overcome these limitations, we performed single-cell RNA sequencing on 53586 adult glioblastoma cells and 22637 normal human fetal brain cells, and compared the lineage hierarchy of the developing human brain to the transcriptome of cancer cells. We find a conserved neural tri-lineage cancer hierarchy centered around glial progenitor-like cells. We also find that this progenitor population contains the majority of the cancer’s cycling cells, and, using RNA velocity, is often the originator of the other cell types. Finally, we show that this hierarchal map can be used to identify therapeutic targets specific to progenitor cancer stem cells. Our analyses show that normal brain development reconciles glioblastoma development, suggests a possible origin for glioblastoma hierarchy, and helps to identify cancer stem cell-specific targets.
Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy (scRNA-seq - Whole tumor)
ViewID:
couturier_et_al_2020_wt
Total cells:
44,050
Number of matched cells:
44050
Cancer stem cells are critical for cancer initiation, development, and treatment resistance. Our understanding of these processes, and how they relate to glioblastoma heterogeneity, is limited. To overcome these limitations, we performed single-cell RNA sequencing on 53586 adult glioblastoma cells and 22637 normal human fetal brain cells, and compared the lineage hierarchy of the developing human brain to the transcriptome of cancer cells. We find a conserved neural tri-lineage cancer hierarchy centered around glial progenitor-like cells. We also find that this progenitor population contains the majority of the cancer’s cycling cells, and, using RNA velocity, is often the originator of the other cell types. Finally, we show that this hierarchal map can be used to identify therapeutic targets specific to progenitor cancer stem cells. Our analyses show that normal brain development reconciles glioblastoma development, suggests a possible origin for glioblastoma hierarchy, and helps to identify cancer stem cell-specific targets.
Cell Types of the Human Retina and Its Organoids at Single-Cell Resolution (Developed organoid)
ViewID:
Cowan_et_al_2020_DO
Total cells:
43,857
Number of matched cells:
43857
Human organoids recapitulating the cell-type diversity and function of their target organ are valuable for basic and translational research. We developed light-sensitive human retinal organoids with multiple nuclear and synaptic layers and functional synapses. We sequenced the RNA of 285,441 single cells from these organoids at seven developmental time points and from the periphery, fovea, pigment epithelium and choroid of light-responsive adult human retinas, and performed histochemistry. Cell types in organoids matured in vitro to a stable “developed” state at a rate similar to human retina development in vivo. Transcriptomes of organoid cell types converged toward the transcriptomes of adult peripheral retinal cell types. Expression of disease-associated genes was cell-type-specific in adult retina, and cell-type specificity was retained in organoids. We implicate unexpected cell types in diseases such as macular degeneration. This resource identifies cellular targets for studying disease mechanisms in organoids and for targeted repair in human retinas.
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