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 transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines
ViewID:
AN1801
Total cells:
43,112
Number of matched cells:
43112
Naïve CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. Here we use quantitative proteomics, bulk RNA-seq, and single-cell RNA-seq of over 40,000 human naïve and memory CD4+ T cells to show that responses to cytokines differ substantially between these cell types. Memory T cells are unable to differentiate into the Th2 phenotype, and acquire a Th17-like phenotype in response to iTreg polarization. Single-cell analyses show that T cells constitute a transcriptional continuum that progresses from naïve to central and effector memory T cells, forming an effectorness gradient accompanied by an increase in the expression of chemokines and cytokines. Finally, we show that T cell activation and cytokine responses are influenced by the effectorness gradient. Our results illustrate the heterogeneity of T cell responses, furthering our understanding of inflammation.
A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer (cohort 1)
ViewID:
ayse_et_al_2021_cohort1_all
Total cells:
175,942
Number of matched cells:
175942
Immune-checkpoint blockade (ICB) combined with neoadjuvant chemotherapy improves pathological complete response in breast cancer. To understand why only a subset of tumors respond to ICB, patients with hormone receptor-positive or triple-negative breast cancer were treated with anti-PD1 before surgery. Paired pre- versus on-treatment biopsies from treatment-naive patients receiving anti-PD1 (n = 29) or patients receiving neoadjuvant chemotherapy before anti-PD1 (n = 11) were subjected to single-cell transcriptome, T cell receptor and proteome profiling. One-third of tumors contained PD1-expressing T cells, which clonally expanded upon anti-PD1 treatment, irrespective of tumor subtype. Expansion mainly involved CD8+ T cells with pronounced expression of cytotoxic-activity (PRF1, GZMB), immune-cell homing (CXCL13) and exhaustion markers (HAVCR2, LAG3), and CD4+ T cells characterized by expression of T-helper-1 (IFNG) and follicular-helper (BCL6, CXCR5) markers. In pre-treatment biopsies, the relative frequency of immunoregulatory dendritic cells (PD-L1+), specific macrophage phenotypes (CCR2+ or MMP9+) and cancer cells exhibiting major histocompatibility complex class I/II expression correlated positively with T cell expansion. Conversely, undifferentiated pre-effector/memory T cells (TCF7+, GZMK+) or inhibitory macrophages (CX3CR1+, C3+) were inversely correlated with T cell expansion. Collectively, our data identify various immunophenotypes and associated gene sets that are positively or negatively correlated with T cell expansion following anti-PD1 treatment. We shed light on the heterogeneity in treatment response to anti-PD1 in breast cancer.
A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer (cohort 2)
ViewID:
ayse_et_al_2021_cohort2_all
Total cells:
50,693
Number of matched cells:
50693
Immune-checkpoint blockade (ICB) combined with neoadjuvant chemotherapy improves pathological complete response in breast cancer. To understand why only a subset of tumors respond to ICB, patients with hormone receptor-positive or triple-negative breast cancer were treated with anti-PD1 before surgery. Paired pre- versus on-treatment biopsies from treatment-naive patients receiving anti-PD1 (n = 29) or patients receiving neoadjuvant chemotherapy before anti-PD1 (n = 11) were subjected to single-cell transcriptome, T cell receptor and proteome profiling. One-third of tumors contained PD1-expressing T cells, which clonally expanded upon anti-PD1 treatment, irrespective of tumor subtype. Expansion mainly involved CD8+ T cells with pronounced expression of cytotoxic-activity (PRF1, GZMB), immune-cell homing (CXCL13) and exhaustion markers (HAVCR2, LAG3), and CD4+ T cells characterized by expression of T-helper-1 (IFNG) and follicular-helper (BCL6, CXCR5) markers. In pre-treatment biopsies, the relative frequency of immunoregulatory dendritic cells (PD-L1+), specific macrophage phenotypes (CCR2+ or MMP9+) and cancer cells exhibiting major histocompatibility complex class I/II expression correlated positively with T cell expansion. Conversely, undifferentiated pre-effector/memory T cells (TCF7+, GZMK+) or inhibitory macrophages (CX3CR1+, C3+) were inversely correlated with T cell expansion. Collectively, our data identify various immunophenotypes and associated gene sets that are positively or negatively correlated with T cell expansion following anti-PD1 treatment. We shed light on the heterogeneity in treatment response to anti-PD1 in breast cancer.
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