Supplementary Materials for - Science · 2018-07-02 · Data visualization Underneath cell...
Transcript of Supplementary Materials for - Science · 2018-07-02 · Data visualization Underneath cell...
immunology.sciencemag.org/cgi/content/full/3/25/eaas9818/DC1
Supplementary Materials for
Metabolic signaling directs the reciprocal lineage decisions of αβ and γδ T cells
Kai Yang, Daniel Bastardo Blanco, Xiang Chen, Pradyot Dash, Geoffrey Neale, Celeste Rosencrance, John Easton,
Wenan Chen, Changde Cheng, Yogesh Dhungana, Anil KC, Walid Awad, Xi Zhi J. Guo, Paul G. Thomas, Hongbo Chi*
*Corresponding author. Email: [email protected]
Published 6 July 2018, Sci. Immunol. 3, eaas9818 (2018)
DOI: 10.1126/sciimmunol.aas9818
The PDF file includes:
Materials and Methods
Fig. S1. Dynamic regulation of nutrient transporters and cell growth in developing thymocytes.
Fig. S2. mTOR signaling is required for thymocyte development.
Fig. S3. RAPTOR deficiency diminishes cell growth, proliferation, and nutrient transporter
expression of ISP cells, but not cell survival.
Fig. S4. RAPTOR controls metabolic gene expression programs in thymocytes.
Fig. S5. RAPTOR deficiency affects γδ T cell development.
Fig. S6. Loss of RHEB or RICTOR does not affect the development of γδ T cells.
Fig. S7. SCAP and HIF1α are dispensable for T cell development.
Fig. S8. MYC is implicated in αβ and γδ T cell development.
Fig. S9. RAPTOR-deficient DN3 cells exhibit increased ROS but normal mitochondrial mass
and membrane potential.
Fig. S10. Modulation of ROS production alters fate choices of DN3 cells.
Fig. S11. mTORC1 activation integrates signals from pre-TCR and NOTCH.
Fig. S12. RAPTOR deficiency alters transcriptional programs and signaling and metabolic
pathways.
Fig. S13. Monocle pseudotime trajectory of single-cell transcriptomics data.
Fig. S14. Loss of RAPTOR or MYC enhances signal strength.
Fig. S15. Schematics of mTORC1 and metabolic control of redox homeostasis and signal
strength in T cell lineage choices.
Reference (54)
Other Supplementary Material for this manuscript includes the following:
(available at immunology.sciencemag.org/cgi/content/full/3/25/eaas9818/DC1)
Table S1 (Microsoft Excel format). Raw data sets.
2
Materials and Methods
Flow cytometry
For analysis of surface markers, cells were stained in PBS containing 2% (wt/vol) BSA, with anti-
CD4 (RM4-5), anti-CD8a (53-6.7), anti-TCRb (H57-597), anti-CD25 (PC61.5), anti-CD44
(1M7), anti-CD62L (MEL-14), anti-CD45.1 (A20), anti-CD45.2 (104), anti-TCRgd (eBioGL3),
anti-CD24 (M1/69), anti-CD73 (eBioTY/11.8), anti-CD5 (53-7.3), anti-CD98 (RL388), and anti-
CD71 (R17217). Intracellular Ki67 (SolA15), phospho-S6 (D57.2.2E), and phospho-4E-BP1
(236B4) were analyzed by flow cytometry according to the manufacturer's instructions. Two
antibodies (C-20, Santa Cruz Biotechnology; and D16D10, Cell Signaling Technology) were used
to measure ID3 expression by flow cytometry, with similar results. For measurement of ROS,
mitochondrial mass and potential, thymocytes were incubated in the medium containing 10 µM
CM-H2DCFDA (5-(and-6)-chloromethyl-2,7-dichlorodihydrofluorescein diacetate acetyl ester;
Life Technologies), 10 nM MitoTracker Deep Red (Life Technologies) or 20 nM TMRM
(tetramethyl rhodamine, methyl ester; ImmunoChemistry Technologies), respectively, for 30 min
at 37°C before staining surface markers. BrdU incorporation was measured in thymocytes from
mice injected with BrdU (1 mg/mouse) for 3 h according to manufacturer’s instructions (BD
Biosciences). Caspase-3 activity was measured using active caspase-3 apoptosis kit (BD
Biosciences). Flow cytometry data were acquired on LSRII or LSR Fortessa (Becton Dickinson),
and analyzed using FlowJo software (Tree Star).
Cell purification and cultures
Lineage-negative WT and Rptor–/– DN3 (CD4–CD8–TCRgd–TCRb–CD25hiCD44–) or DN3a cells
(CD4–CD8–TCRgd–TCRb–CD25hiCD44–CD28–) were sorted on a MoFlow (Beckman-Coulter) or
3
Reflection (i-Cyt). DN3 cells were used for Seahorse assay and in vitro cultures with OP9 or OP9-
DL1 cells in the presence of IL-7 (0.5 ng/ml) and Flt3L (5 ng/ml) for 3 days. For metabolic
perturbation, DN3a cells were co-cultured with OP9-DL1 cells in the presence of rapamycin (10
ng/ml), DCA (10 mM), galactose (0.5 mM) and galactose oxidase (GAO, 0.3 U/ml), GSH (5 mM),
or NAC (20 mM) for 3-5 days. For T cell differentiation under a stromal cell-free culture condition
(36), sorted DN3a cells were cultured in DLL4 (2 µg/ml)-coated plates, in the presence of CXCL12
(100 ng/ml) or IL-7 (1 ng/ml) to promote aβ or gd T cell development, respectively, with or
without the indicated inhibitors for 4 days.
Gene expression profiling by microarray analysis
RNA samples from freshly isolated ISP (WT, n = 4; Rptor–/–, n = 3), DN3a, or gd T cells (WT, n
= 3; Rptor–/–, n = 3) were analyzed with the Affymetrix Mouse Gene 2.0 ST GeneChip array. Gene
set enrichment analysis (GSEA) within hallmark gene sets (MSigDB) or canonical pathways
(MSigDB c2.CP gene sets for canonical pathways) was performed as described (22). Differentially
expressed transcripts were identified by ANOVA (Partek Genomics Suite v6.5) and the Benjamini-
Hochberg method was used to estimate the false discovery rate (FDR) as described previously
(51). Lists of differentially expressed genes by > 0.5 log2 difference or more were analyzed using
Ingenuity Pathway Analysis (Qiagen, Inc.) for upstream transcription regulators.
RNA and immunoblot analysis
Real-time PCR analysis was performed with primers and probe sets from Applied Biosystems as
described (51). Immunoblots were performed as described (54) using the following antibodies:
EGR1 (44D5), RAPTOR (24C12), p-ERK (9101), ID3 (D16D10), p-p65 (all from Cell Signaling
4
Technology), β-ACTIN (AC-15; Sigma); ATP5A, UQCRC2, MTCO1, SDHB, and NDUFB8
(using total OXPHOS antibody cocktail from Abcam).
Metabolic assays
OCR and ECAR were measured in XF media (non-buffered DMEM containing 5 mM glucose, 2
mM L-glutamine and 1 mM sodium pyruvate), under basal conditions and in response to 1 µM
oligomycin, 2 µM fluoro-carbonyl cyanide phenylhydrazone (FCCP) and 1 µM Rotenone using
the XF-24 Extracellular Flux Analyzer (Seahorse Bioscience). For glycolytic rate, the various
subsets of thymocytes were cultured in Click’s medium containing 1 µCi [3-3H]-glucose for 6 h.
Glycolytic flux was determined by measuring the detritiation of [3-3H]-glucose, as we previously
described (28).
Statistical analysis for biological experiments
P values were calculated by two-tailed Mann-Whitney test, two-tailed unpaired Student’s t test, or
ANOVA using GraphPad Prism, unless otherwise noted. P value of < 0.05 was considered
significant. All error bars represent the SEM.
Single-cell multiplex RT-PCR and sequencing of TCRγ chains
Thymocytes from WT or Rptor–/– mice were harvested and resuspended. After depletion of CD4+
cells using CD4 (L3T4) MicroBeads (Miltenyi Biotec), the remaining cells were stained with anti-
CD4, anti-CD8, anti-NK1.1, anti-TCRb, anti-TCRgd, anti-Ter119, anti-B220 and anti-Gr1 in 200
µl of sorting buffer (PBS containing 0.1% BSA, Fraction V (Gibco)) on ice, in dark for 20 min.
Following staining, cells were washed three times and resuspended in sorting buffer (containing
5
200 units of RNAsin/ml (Promega)) and filtered through 40 µM cell strainer prior to sorting. γδ T
cells (TCRgd+CD4–CD8–NK1.1–TCRb–B220–Ter119–Gr1–) were sorted singly into each well of a
384-well plate (Eppendorf) using an iCyt Synergy cell sorter (Sony) with the following parameters:
Multi-drop sort OFF, Multi-drop exclude OFF, Division 10, Center sort %: 90. At least one column
of the 384-well plates was left unsorted to use as negative controls for PCR. After sorting, the
plates were sealed using plate sealer film (MicroAmp, Applied Biosystems) and centrifuged at 500
g for 3 min and stored at -80°C until further amplification. Unbiased amplification of TCRγ chain
at the single cell level was carried out as per method described before (24). Briefly, the cDNA
synthesis was performed directly from single cells without any RNA extraction step using the
SuperScript VILO cDNA synthesis kit (Thermo Scientific) in a volume of 1 µl of reaction mix
(0.2 µl of 5´ VILO reaction mix, 0.1 µl of 10´ SuperScript® Enzyme mix, 0.1 µl of 1% Triton X-
100 (Sigma)). Following reverse transcription, a multiplex nested PCR was carried out using a Taq
polymerase based PCR kit (Qiagen). The protocol for the first and second round PCR (except for
the primer components), sequencing, and data analysis were performed as previously described
(24, 25). The primers for the mouse TCRγ chain were used as below.
Name External Internal
TRGV1-3 For GCAGCTGGAGCAAACTG CTGAATTATCGGTCACCAG
TRGV4 For CAAATATCCTGTAAAGTTTTCATC GTTTAGAGTTTCTATTATATGTCCTTGCAAC
TRGV5 For GATATCTCAGGATCAGCTCTCC TACCCGAAGACCAAACAAGAC
TRGV6 For TCACCTCTGGGGTCATATG AGAGGAAAGGAAATACGGC
TRGV7 For CAACTTGGAAGAAAGAATAATGTC CACCAAGCTAGAGGGGTC
TRGC Rev CTTTTCTTTCCAATACACCC TCDGGAAAGAACTTTTCAAGG
Single-cell RNA sequencing (scRNA-seq)
6
Lineage-negative thymocytes (Ter119–Gr1–CD19–NK1.1–CD4–CD8–TCRb–TCRgd–CD44–;
representative of DN3-DN4 cells) were sorted into culture medium, spun down, and re-suspended
in 100 µl of 1´ PBS + 0.04% BSA. The cells were counted and examined for viability using a
Luna Dual Florescence Cell Counter (Logos Biosystems). Single-cell suspensions were loaded
onto the Chromium Controller according to their respective cell counts to generate 6000 single-
cell GEMs (gel beads in emulsion) per sample. Each sample was loaded into a separate channel.
Libraries were prepared using the Chromium Single Cell 3’ v2 Library and Gel Bead Kit (10x
Genomics). The cDNA content of each sample after cDNA amplification of 12 cycles was
quantified and quality checked using a High-Sensitivity DNA chip with a 2100 Bioanalyzer
(Agilent Technologies) to determine the number of PCR amplification cycles to yield sufficient
library for sequencing. After library quantification and quality check by DNA 1000 chip (Agilent
Technologies), samples were diluted to 3.5 nM for loading onto the HiSeq 4000 (Illumina) with a
2 ´ 75 Paired-end kit using the following read length: 26 bp Read1, 8 bp i7 Index, and 98 bp
Read2. An average of 400,000,000 reads per sample was obtained (~approximately 80,000 reads
per cell).
Analysis for scRNA-seq data
Alignment, barcode assignment, and UMI counting
The Cell Ranger 1.3 Single-Cell Software Suite (10x Genomics) was implemented to process the
raw sequencing data from the Illumina HiSeq run. This pipeline performed de-multiplexing,
alignment (mm10), and barcode processing to generate gene-cell matrices used for downstream
analysis. Specifically, data for 2 WT and 3 Rptor–/– samples were combined, and UMIs mapped to
genes encoding ribosomal proteins were removed. Cells with low (bottom 2.5%, potentially dead
7
cells with broken membrane) or high (top 2.5%, potentially two or more cells in a single droplet)
UMI counts were filtered. On average, 4,123 mRNA molecules (UMIs) were captured in a cell
(median: 3,448, range: 1,703–9,999), representing 2,110 genes (median: 1,968, range: 59–3,916).
The expression level of each gene is normalized to 10,000 UMIs per cell and log transformed by
adding 0.25 to the expression matrix.
Clustering
The subpopulation structure of the whole dataset was inferred using Latent Cellular State Analysis
(LCA), a novel clustering algorithm developed in house for analyzing large-scale scRNA-seq data
(manuscript in preparation). Briefly, LCA first used singular value decomposition (SVD) to derive
latent cellular states from the expression matrix for individual cells. The number of significant
cellular states was determined using the Tracy-Widom test on eigenvalues. A modified version of
spectral clustering was performed on the significant cellular states of individual cells (cellular
states explained by total UMIs ignored) with different number of clusters (2-30). The optimal
number of clusters was manually selected from top models determined by the silhouette measure
for solutions with different number of clusters.
Data visualization
Underneath cell variations were visualized in a 2D projection by t-distributed stochastic neighbor
embedding (tSNE). Expression of individual genes or pathway scores were color coded (gray: not
expressed; from low to high: blue-green-yellow-red) for each cell on tSNE plots.
Generation of gene signatures and statistical analysis for over-representation
8
We generated DN3a signature from the genes specifically expressed in DN3a but not in DN3b,
DN3b-DN4 or DN4 cells (92 genes with FDR < 0.05, log2 fold change > 1.5 for each comparison)
(45) (GSE15907). The pre-TCR signaling signature was generated from genes upregulated in
DN3b cells compared with DN3a cells (75 genes with FDR < 0.05, log2 fold change > 2)
(GSE15907). The gd T cell gene signature was generated from genes selectively expressed by gd
T cells (46) (121 genes upregulated in gd T cells, but not DN3b cells, as compared with DN3a
cells, with FDR < 0.05, log2 fold change > 2). For gene signatures of metabolic and signaling
pathways, the list of 2,088 genes with strong and dynamic expression in thymocyte differentiation
(45) was used to intersect with gene sets from MSigDB (hallmark mTORC1 signaling, hallmark
MYC targets v1, hallmark glycolysis, KEGG NOTCH signaling pathway, and PDGF ERK DN.V1
DN) for refined gene signatures relevant to thymocyte development. For scRNA-seq analysis,
genes from these signatures were retained if they were expressed in at least 10% of cells in any
cluster. Gene signature activity was calculated as the average value for all retained genes. Relative
over-representation of gene signatures for a specific cluster (Fig. 6D) was determined using one-
tailed Mann-Whitney test (cells in a specific cluster vs all other cells). Differential activity of a
gene signature or individual gene expression between defined clusters was determined using two-
tailed Mann-Whitney test.
Pseudo-temporal analysis
Monocle 2 (47) was used to generate the single cell pseudo-temporal trajectories with default
settings.
Statistical analysis for WT and Rptor–/– enrichment for individual clusters
9
The numbers of cells in and outside a specific cluster were counted for each mouse. Logistic
regression was used to assess the association between the cluster cell counts and the independent
predictor of WT and Rptor–/– samples (Fig. 6E).
10
Fig. S1
Fig. S1. Dynamic regulation of nutrient transporters and cell growth in developing thymocytes. (A) Diagram of T cell development in the thymus. Hematopoietic stem cells (HSC) derived from the bone marrow migrate to the thymus and progress through well-defined developmental stages to become ab or gd T cells. Double-negative cells (DN, without expression of CD4 and CD8) at early thymocyte development can be divided into four stages, with the DN3 stage being a crucial checkpoint for T cell lineage choices. DN3 cells with successful b-selection continue their developmental process by transitioning through the DN4, ISP (immature single-positive) and DP (double-positive) stages before becoming CD4 or CD8 single-positive cells (SP). Alternatively, DN3 cells complete gd-selection to become gd T cells, followed by induction of CD73 expression to mark fully committed gd T cells. (B and C) Relative expression of CD98 (B) and CD71 (C) (MFI in DN3 cells is set to 1) in thymocyte subsets. (D) Plot of cell size (FSC) in thymocyte subsets. Data are representative of at least five independent experiments (B to D).
D
FSC0 50K 100K 150K 200K 250K
0
20
40
60
80
100 DN3DN4ISPDP
1.19e51.3e51.34e597852
0
2
4
6
8
Rel
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e C
D71
DN4
DN3a
DN3b
DP
DN3DN4ISPDP
0
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8CB
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D98
0
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DN4
DN3a
DN3b
DP
DN3DN4ISPDP
Cel
ls
HSCT lineage
commitmentDN1 DN2
DN3 (DN3a and DN3b)
gd T cells Committed gd T cells (CD73+)
DN4 ISP DP
CD4 SP
CD8 SP⍺b lineage
gd lineage
Lineage choices
A
11
Fig. S2
Fig. S2. mTOR signaling is required for thymocyte development. (A and B) Analysis of RAPTOR mRNA (A) and protein (B) expression in thymocyte subsets from WT and Rptor–/– mice. (C) Frequencies of thymocyte subsets from WT and Rptor–/– mice. Each symbol represents an individual mouse (WT, n = 7; Rptor–/–, n = 6). (D) Cellularity of CD4+ and CD8+ T cells in the spleen of WT and Rptor–/– mice. Each symbol represents an individual mouse (n = 3 each group). Data are means ± SEM. ns, not significant; *P < 0.05, **P < 0.005, and ***P < 0.0001. One-way ANOVA with Tukey’s test (C), or two-tailed unpaired t test (D). Data are representative of at least three independent experiments (A to D).
A
DN3 DN4 ISP DP
Rpt
or m
RN
A WT Rptor–/–
0 0.5 1.0 1.5 2.0 B
WT
Rpt
or–/
– W
T R
ptor
–/–
DN3 DN4
b-ACTIN RAPTOR
D
0246810
Cel
l num
ber (×1
06 )
** *
0 2 4 6 8
10 WT Rptor–/–
CD4+ CD8+
Thym
ocyt
es (%
)
DN DP CD8 SP
CD4 SP
***
***
ns ***
0 20 40 60 80
100 C WT Rptor–/–
12
Fig. S3
Fig. S3. RAPTOR deficiency diminishes differentiation, proliferation, and nutrient transporter expression of ISP cells, but not cell survival. (A) ISP cells from WT and Rptor–
/– mice were cultured with OP9-DL1 cells for 1 day, followed by analysis of CD4 and CD8 (left panel) and CD24 and CD8 expression on CD4–CD8+ ISP cells (right panel). (B) Caspase-3 activity in thymocyte subsets in WT and Rptor–/– mice. (C) BrdU incorporation in thymocyte subsets in WT and Rptor–/– mice pulsed with BrdU for 3 h. (D) Gating strategy for DN3a (Lin–CD4–CD8–CD25+CD44–CD27–) and DN3b (Lin–CD4–CD8–CD25+CD44–
CD27+) cells. (E) Relative phosphorylation of S6 and 4E-BP1 (MFI in DN3a cells is set to 1) in DN3a and DN3b cells from WT mice. (F and G) Plots of FSC, CD71 and CD98 (F), and icTCRb (G) in thymocyte subsets in WT and Rptor–/– mice. Data are means ± SEM. Data are representative of at least three independent experiments (A to G). Numbers indicate percentage of cells in gates.
C
BrdU
Cel
ls
DN4DN3 ISP
100
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0
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5.18
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4.98
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8.91
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12.1
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104
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5
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39
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Rptor–/–
DP
100
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104
0
1000
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0.67
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3.95
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CD4
DN3 DN4 DPISP
WT
Cas
pase
-3
CD8
Rptor–/–
0 102 103 104 105
0
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0.779
0 102 103 104 105
0
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0.861
0 102 103 104 105
0
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0 102 103 104 105
0
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0 102 103 104 105
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0 102 103 104 105
0
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1.14
0 102 103 104 105
0
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0.469
0 102 103 104 105
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0.472
F
0 102
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0 102
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0
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0 102
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CD98
0 102
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0 102
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CD71C
ells
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0 50K 100K 150K 200K 250K
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0 50K 100K 150K 200K 250K
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DN3a DN4DN3b
0 102
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0 102
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0 50K 100K 150K 200K 250K
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ISPWT Rptor–/–
GDN3a DN4DN3b
icTCRb
Cel
ls
ISPWT Rptor–/–
0 102 103 104 1050
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D
CD
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CD27
FSC
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CD25
DN3
Lin–CD4–CD8–E
0
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3DN3aDN3a
DN3b
DN3bDN3a
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Rel
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e p-
S6
Rel
ativ
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4E-B
P1
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CD
8
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0 102 103 104 105
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0.581
26.2
0.894
60.7
0 102 103 104 105
0
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0.957
11.8
0.811
75.7
CD24
CD
8
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0 102 103 104 105
0
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0 102 103 104 105
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1.42
A
13
Fig. S4
Fig. S4. RAPTOR controls metabolic gene expression programs in thymocytes. (A) List of the top 10 downregulated gene sets in the databases of hallmarks and canonical pathways (MSigDB) in Rptor–/– ISP cells, as identified by gene set enrichment assay (GSEA) of microarray profiles (WT, n = 4; Rptor–/–, n = 3). NES, normalized enrichment score; FDR, false discovery rate. No significantly upregulated gene sets (FDR < 0.05) were identified in these databases. (B) Analysis of Idi1, Hmgcs1 and Sqle mRNA expression in WT and Rptor–/– ISP cells. (C) List of the top 10 potential transcription regulators downregulated in Rptor–/– ISP cells as identified by Ingenuity Pathway Analysis (IPA) of microarray profiles (WT, n = 4; Rptor–/–, n = 3). Data are from one experiment (A, C), or representative of three independent experiments (B).
Hallmark gene sets NES FDR q-valHALLMARK_MTORC1_SIGNALING -2.704 <0.001HALLMARK_MYC_TARGETS_V1 -2.608 <0.001HALLMARK_MYC_TARGETS_V2 -2.476 <0.001HALLMARK_OXIDATIVE_PHOSPHORYLATION -2.256 <0.001HALLMARK_TNFA_SIGNALING_VIA_NFKB -2.231 <0.001HALLMARK_CHOLESTEROL_HOMEOSTASIS -2.174 <0.001HALLMARK_UNFOLDED_PROTEIN_RESPONSE -1.885 5.43E-04HALLMARK_UV_RESPONSE_UP -1.874 4.75E-04HALLMARK_FATTY_ACID_METABOLISM -1.801 0.001095HALLMARK_E2F_TARGETS -1.689 0.004129Canonical pathway gene sets NES FDR q-valREACTOME_M_G1_TRANSITION -2.470 <0.001REACTOME_AMYLOIDS -2.451 <0.001REACTOME_RNA_POL_I_PROMOTER_OPENING -2.409 <0.001REACTOME_INFLUENZA_LIFE_CYCLE -2.380 <0.001REACTOME_CHOLESTEROL_BIOSYNTHESIS -2.360 <0.001REACTOME_RESPIRATORY_ELECTRON_TRANSPORT_ATP_SYNTHESIS_BY_CHEMIOSMOTIC_COUPLING_AND_HEAT_PRODUCTION_BY_UNCOUPLING_PROTEINS_ -2.347 <0.001REACTOME_SYNTHESIS_OF_DNA -2.344 <0.001REACTOME_S_PHASE -2.334 <0.001REACTOME_TCA_CYCLE_AND_RESPIRATORY_ELECTRON_TRANSPORT -2.298 <0.001REACTOME_ASSEMBLY_OF_THE_PRE_REPLICATIVE_COMPLEX -2.296 <0.001
A
B
0.0
0.5
1.0
1.5
Idi1mRNA
0.0
0.5
1.0
1.5
Hmgcs1
mRNA
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1.5
SqlemRNA
WTRptor–/–
0
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1.0
1.5
0
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1.0
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0
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1.0
1.5
CUpstream regulators P-valueMYC 1.23E-20FOXO1 2.79E-14CREB1 3.66E-14SREBF2 5.16E-13SREBF1 1.37E-11TRIM24 1.93E-09MYCN 4.42E-06PPARGC1B 1.71E-05HNF4A 2.11E-05ATF4 2.65E-05
14
Fig. S5
ATC
Rgd
+ /TC
Rb+
0.00
0.05
0.10
0.15
0.20
00.050.10
0.15
0.20***
WT Rptor–/–
E
I
K
J
TCRgd0 102 103 104 105
0
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105
0.0859
0 102 103 104 105
0
102
103
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0.115
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pase
-3
WT Rptor–/–F
CD45.2+ Rptor–/–CD45.2+ WT WT/CD45.1+ Rptor–/–/CD45.1+
CD4
TCRgd
776 656 745 785
23781 905 33965 29973
CD45.2+ WTCD45.2+ Rptor–/–
14403/31904/12727/11925
Cel
ls
WT/CD45.1+
Rptor–/–/CD45.1+
CD73
0
20
40
60
Vg1.
1
WT Rptor–/–
Vd6.30 102 103 104 105
0
102
103
104
105 27.3 2.33
68.8 1.560 102 103 104 105
0
102
103
104
105 40.1 3.07
55.2 1.58
WT Rptor–/–
ns
Vg1.
1+(%
)
0
20
40
60
0
20
40
60
80L ns
Vg2
Vd40 102 103 104 105
0
102
103
104
105 28.3 10.9
53.3 7.540 102 103 104 105
0
102
103
104
105 26.3 10.5
10.852.4
WT Rptor–/–
Vg2+
(%)
WT Rptor–/–0
20
40
80
60
Freq
uenc
y (%
)
Vg1 Vg2 Vg3 Vg4 Vg5 Vg6 Vg7
Thymus
TRGV1
TRGV2
TRGV3
TRGV4
TRGV5
TRGV6
TRGV70
20
40
60
80
Frequency (
%) WT
KORptor–/–
Thymus
TRGV1
TRGV2
TRGV3
TRGV4
TRGV5
TRGV6
TRGV70
20
40
60
80
Frequency (
%) WT
KOWT
Thymus
TRGV1
TRGV2
TRGV3
TRGV4
TRGV5
TRGV6
TRGV70
20
40
60
80
Frequency
(%) WT
KO
0
20
40
80
60
CD40 102 103 104 105
0
102
103
104
105
66.5
3.09
0 102 103 104 105
0
102
103
104
105
23.4
6.19
TCRgd
WT Rptor–/–
CD730 102 103 104 105
0
20
40
60
80
100
Cel
ls
16521/40331
Rptor–/–
WT
0
1
2
3
TCRgd
+(×
103 )
ns
WT Rptor–/–
CD
4+(×
104 )
0
5
10
15
20
25
WT Rptor–/–
***To
tal n
umbe
rs (×
104 )
TCRgd
+ CD
73+
(×10
3 )
0
1
2
3 ***
WT Rptor–/–0
20
40
60 *
WT Rptor–/–0
20
40
60
80
100
WT Rptor–/–
*
CD
4+(%
)
0
1
2
3
4
5
WT Rptor–/–
*
TCRgd+
(%)
0
20
40
60
80
100
WT Rptor–/–
*
TCRgd
+ CD
73+
(%)
0
20
40
60
02040
8060
100
02040
8060
100
012345
0
1
2
3
0
1
2
3
05
10
2015
25
0.0
0.5
1.0
1.5
Rpt
or m
RN
A0
0.5
1.0
1.5
B
WT Rptor–/–
C
FSC
Cel
ls
CD98CD710 200 400 600 800 1000
0
20
40
60
80
100
385/341
100 101 102 103 1040
20
40
60
80
100
82.1/44.4
100 101 102 103 1040
20
40
60
80
100
500/363D
WT Rptor–/–
Ki67
Cel
ls
100 101 102 103 1040
3
6
9
12
80.4
100 101 102 103 1040
5
10
15
20
25
54.8
Rptor–/–WT
G H
15
Fig. S5. RAPTOR deficiency affects gd T cell development. (A) Ratio of TCRgd+ to TCRb+ cells in WT and Rptor–/– mice. Each symbol represents an individual mouse (n = 8 each group). (B) Analysis of Rptor mRNA in WT and Rptor–/– TCRgd+ cells (Rptor mRNA in WT is normalized to 1). (C) Plots of FSC, CD71 and CD98 in WT and Rptor–/– TCRgd+ cells, with MFI plotted above graphs. (D) Plot of Ki67 expression in WT and Rptor–/– TCRgd+ cells. Numbers indicate the frequency of the Ki67+ population. (E) Caspase-3 activity of WT and Rptor–/– TCRgd+ cells. (F) WT or Rptor–/– cells were cultured with OP9-DL1 cells, followed by examination of CD4+ and TCRgd+ T cells (left), and CD73 expression on TCRgd+ cells with MFI plotted above the graph (right). (G, H) Frequencies (G) and numbers (H) of total cells, CD4+ T cells, TCRgd+ cells, and committed gd T cells (TCRgd+CD73+) in F. (I) Left: WT (CD45.2+) or Rptor–/– (CD45.2+) DN3a cells were cultured with CD45.1+ DN3a cells in the presence of OP9-DL1 cells, followed by examination of TCRgd+ and CD4+ T cells, with cell numbers included in the graphs. Right: Expression of CD73 on TCRgd+ cells with MFI plotted above the graph. (J) Single cell analysis of diverse TCRg chains expressed in thymic TCRgd+ T cells of WT and Rptor–/– mice. The Vg usage derived from the CDR3g sequence data is plotted. (K and L) Flow cytometry of Vg1.1+Vd6.3+ (K) and Vg2+Vd4+ (L) TCRgd+ T cell subsets in the thymus. Right, frequency of Vg1.1+ or Vg2+ cells. Each symbol represents an individual mouse (WT, n = 4; Rptor–/–, n = 3). Data are means ± SEM. *P < 0.05 and ***P < 0.001. Two-tailed unpaired t test (A), paired t test (G), one-tailed unpaired t test (H), or two-tailed Mann-Whitney test (K, L). Data are representative of at least three independent experiments (A, C to I), or two independent experiments (B, J to L). Numbers indicate percentage of cells in the quadrants or gates.
16
Fig. S6
Fig. S6. Loss of RHEB or RICTOR does not affect the development of gd T cells. (A) Left: Flow cytometry of thymocyte subsets of WT and Rheb–/– mice. Right: Cellularity of total thymocytes. Each symbol represents an individual mouse (n = 5 each group). (B) Flow cytometry of TCRgd+ cells in WT and Rheb–/– mice (left). Frequency (middle) and cellularity (right) of TCRgd+ cells in the thymus of WT and Rheb–/– mice (n = 3 each group). (C) Left: Flow cytometry of TCRgd+ cells in WT, Rptor–/–, Rictor–/– and Rptor–/–Rictor–/– thymocytes. Right: Frequency and cellularity of TCRgd+ cells and total thymocyte cellularity in the indicated mouse groups. Each symbol represents an individual mouse (WT, n = 7; Rptor–/–, n = 5; Rictor–/–, n = 3; Rptor–/–Rictor–/–, n = 7). (D) Flow cytometry of CD73 and CD24 expression on TCRgd+ cells in the thymus from the indicated mice. Data are means ± SEM. *P < 0.01, **P < 0.001, and ***P < 0.0001. Two-tailed unpaired t test (A, B for cellularity analyses), two-tailed Mann-Whitney test (B for frequency analysis), or one-way ANOVA with Tukey’s test (C). Data are representative of two independent experiments (D), or at least three independent experiments (A to C). Numbers indicate percentage of cells in quadrants or gates.
0
50
100
150
200
0.0
0.2
0.4
0.6
0
1
2
3
4
TCRgd
+(%
)
WT Rptor–/– Rptor–/–Rictor–/–Rictor–/–
100 101 102 103 104100
101
102
103
104
0.437
100 101 102 103 104100
101
102
103
104
1.77
100 101 102 103 104100
101
102
103
104
0.354
100 101 102 103 104100
101
102
103
104
3.59
WTRptor–/–
Rictor–/–
Rptor–/–
Rictor–/–
0
1
2
3
4WT
Raptor ko
Rictor ko
DKo
**
***ns
TCRgd
TCRb
C
0 102 103 104 105
0
102
103
104
105
0.237
0 102 103 104 105
0
102
103
104
105
0.433
WT Rheb–/–
TCRb
TCRgd
0.0
0.2
0.4
0.6
TCRgd
+(%
)
WT Rheb–/–
ns
0
0.2
0.4
0.6
Thym
ocyt
es (�
106 )
0
50
100
150
200
0 102 103 104 105
0
102
103
104
105 2.19 88.8
1.9 5.370 102 103 104 105
0
102
103
104
105 2.65 85
3.92 6.29
CD4
CD
8
WT Rheb–/–
A B
0
50
100
150
200
WT Rheb–/–
ns
0.0
0.2
0.4
0.6
0.8
0
0.2
0.4
0.6
0.8
TCRgd
+(�
106 )
WT Rheb–/–
ns
D
TCRgd
+(×
106 ) *
ns
0 102 103 104 105
0
102
103
104
105 72.2 19
6.981.790 102 103 104 105
0
102
103
104
105 72.1 18.4
7.711.78
WT Rictor–/–
0 102 103 104 105
0
102
103
104
105 63.5 32.5
2.571.47
Rptor–/–Rictor–/–
0 102 103 104 105
0
102
103
104
105 52.1 37.5
8.042.36
Rptor–/–
CD73
CD
24
Thym
ocyt
es (×
106 )
***ns
*****
17
Fig. S7
Fig. S7. SCAP and HIF1a are dispensable for T cell development. (A to D) Analysis of thymocyte development of WT and Scap–/– mice. (A) Flow cytometry (left) of thymocyte subsets in WT and Scap–/– mice. Frequencies (middle) and cellularity (right) of thymocyte subsets. Each symbol represents an individual mouse (n = 3 each group). (B) Flow cytometry of DN1-4 subsets (gated on lineage-negative DN cells) of WT and Scap–/– thymocytes. (C) Flow cytometry of ISP cells in WT and Scap–/– mice. (D) Flow cytometry of TCRgd+ cells in WT and Scap–/– thymocytes. (E and F) Flow cytometry of thymocyte subsets (E), and TCRgd+ cells (F) in the thymus of WT and Hif1a–/– mice. Data are means ± SEM. One-way ANOVA with Tukey’s test (A). Data are representative of two independent experiments (A to F). Numbers indicate percentage of cells in quadrants or gates.
A
100 101 102 103 104100
101
102
103
104
3.3281.3
1.89 11.3100 101 102 103 104
100
101
102
103
104
2.3985.4
8.271.76
CD4
CD
8Scap–/–WT
ns
0
15
30100
150
200
Thym
ocyt
es(�
106 )
DN DP CD8SP
CD4SP
ns nsns
WTScap–/–
ns
01530
100150200
C
0 102 103 104 105
0
102
103
104
105
91.2
8.68
CD8
TCRb
Scap–/–WT
0 102 103 104 105
0
102
103
104
105
94
5.77
B
100 101 102 103 104100
101
102
103
104
0.585 1.55
37 60.8100 101 102 103 104
100
101
102
103
104
0.526 1.17
36.4 61.9
CD25
CD
44
Scap–/–WTD
100 101 102 103 104100
101
102
103
104
0.167
100 101 102 103 104100
101
102
103
104
0.159
TCRb
Scap–/–WT
TCRgd
100 101 102 103 104100
101
102
103
104
1.3 89.3
2.26 5.68100 101 102 103 104
100
101
102
103
104
1.42 87.3
2.13 7.31
CD4
CD
8
Hif1a–/–WTF
100 101 102 103 104100
101
102
103
104
0.157
100 101 102 103 104100
101
102
103
104
0.17
TCRgd
TCRb
Hif1a–/–WTE
0510156080100
Thym
ocyt
es (%
)
DN DP CD8SP
CD4SP
ns
ns
ns
ns
05
101560
10080
18
Fig. S8
Fig. S8. MYC is implicated in ab and gd T cell development. (A) Left: Flow cytometry of thymocyte subsets in WT and Myc–/– mice. Right: Cellularity of total thymocytes. Each symbol represents an individual mouse (n = 4 each group). (B) Flow cytometry of DN1-4 subsets (gated on lineage-negative DN thymocytes) in WT and Myc–/– mice. (C) Cellularity of DN subsets in WT and Myc–/– thymocytes. Each symbol represents an individual mouse (n = 3 each group). (D) Plots of icTCRb in the indicated WT and Myc–/– DN subsets. (E) Cellularity of TCRgd+ cells in the thymus of WT and Myc–/– mice. Each symbol represents an individual mouse (n = 4 each group). Data are means ± SEM. *P < 0.05, and **P < 0.005. Two-tailed unpaired t test (A, C, E). Data are representative of at least three independent experiments (A to E). Numbers indicate percentage of cells in quadrants or gates.
B
CD
44
0 102 103 104 105
0
102
103
104
105 1.6 4.72
29.9 63.90 102 103 104 105
0
102
103
104
105 0.0655 1.03
10.2 88.7
CD25
WT Myc–/–A
100 101 102 103 104100
101
102
103
104
1.989.4
5.51.45
100 101 102 103 104100
101
102
103
104
1.6118.8
48.910.3
CD
8Myc–/–WT
CD40
100
200
300
WTThym
ocyt
es(�
106 ) **
Myc–/–
DC
Cel
ls0 102 103 104 105
0
20
40
60
80
100
0 102 103 104 1050
20
40
60
80
100
DN3a DN3b
icTCRb
WT Myc–/– E
TCRgd
+(�
106 )
0.0
0.2
0.4
0.6
0.8
WT
ns
00.20.40.60.8
Myc–/–0.00
0.08
0.160.2
5.2
10.2
DN
sub
sets
(�10
6 )
00.080.16
5.20.2
DN1 DN2 DN3 DN4
nsns
*ns
WTMyc–/–
10.2
19
Fig. S9
Fig. S9. RAPTOR-deficient DN3 cells exhibit increased ROS but normal mitochondrial mass and membrane potential. (A) The expression of proteins involved in OXPHOS in DN3 and DN4 cells of WT and Rptor–/– mice. (B) Measurement of glycolytic activity in WT and Rptor–/– DN3 cells. DPM: disintegrations per minute. (C) Relative MFI of ROS in thymocyte subsets of WT and Rptor–/– mice (MFI of WT DN3 cells is set to 1). (D and E) Left: Plots of MitoTracker (D) and TMRM (E) in thymocyte subsets in WT and Rptor–/– mice. Right: Relative MFI of MitoTracker (Mito) (D) and TMRM (E) in thymocyte subsets of WT and Rptor–/– mice (MFI of WT DN3 cells is set to 1). Data are means ± SEM. *P < 0.0001. Two-way ANOVA with Bonferroni’s test (C to E). Data are representative of two independent experiments (A), or representative/combination of two independent experiments (B to E).
0.0
0.5
1.0
1.5WTKO
DN3 DN4 ISP DP
ns ns
ns
ns
WTRptor–/–
0
0.5
1.0
1.5
Rela
tive
Mito
E
0 102 103 104 1050
20
40
60
80
100
TMRM
Cells
WT Rptor–/–
0.0
0.5
1.0
1.5WTRaptor ko
Rela
tive
TMRM
DN3 DN4 ISP DP
ns ns
nsns
0
0.5
1.0
1.5 WTRptor–/–
WT
Rptor
–/–
WT
Rptor
–/–
DN3 DN4
ATP5AUQCRC2
MTCO1SDHB
NDUFB8b-ACTIN
A
0
1
2
3WTRaptor ko
DN3 DN4 ISP DP
*
Rela
tive
ROS
ns ns ns
0
1
2
3 WTRptor–/–
C
D
Cells
WT Rptor–/–
MitoTracker0 102 103 104 105
0
20
40
60
80
100
0
100
200
300
3 H-H
2O (D
PM)
0
100
200
300
WTRptor–/–
B
20
Fig. S10
Fig. S10. Modulation of ROS production alters fate choices of DN3 cells. (A) Left: WT DN3a cells were co-cultured with OP9-DL1 cells in the absence or presence of Gal/GAO, followed by analysis of TCRgd and CD4 expression. Right: Frequencies and numbers of CD4+ and TCRgd+ cells (n = 5 each group). (B) Top: WT DN3a cells were co-cultured with OP9-DL1 cells with or without DCA treatment, followed by analysis of TCRgd and CD4 expression. Bottom: Fold change of the ratio of TCRgd+ to CD4+ cells (WT without DCA treatment is set to 1, n = 3 each group). (C) Analysis of ROS production in WT DN3a cells co-cultured with OP9-DL1 cells in the absence or presence of DCA for 3 days, with MFI plotted in graphs. (D) Left: WT DN3a cells were cultured with plate-bound NOTCH ligand DLL4 in the presence of CXCL12 and treated with the indicated inhibitors, followed by examination of CD4 and TCRgd expression. Right: Frequency of TCRgd+
cells. (E) WT DN3a cells were co-cultured with OP9-DL1 cells in the absence or presence of NAC or GSH for 5 days, and analyzed for cellularity of total cells in the culture (n = 3 each group). (F) WT DN3a cells were cultured with plate-bound NOTCH ligand DLL4 in the presence of IL-7 and treated with either NAC or GSH, followed by examination of TCRgd+ frequency (left; n = 4 each group) and cellularity (right; n = 3 each group). (G) MFI of CD73 on TCRgd+ cells in F. (H) WT and Rptor–/– DN3a cells were co-cultured with OP9-DL1 cells in the absence or presence of NAC for 3 days, followed by analysis of live cells (7-AAD– cells). Data are means ± SEM. *P < 0.05, **P < 0.01, ***P < 0.0001. Two-tailed Mann-Whitney test (A for frequency analysis), two-tailed unpaired t test (A for cellularity analysis), one-tailed unpaired t test (B), or one-way ANOVA with Tukey’s test (E to G). Data are representative of at least three independent experiments (A to H). Numbers indicate percentage of cells in gates.
0
20
40
60
80
100A B
0 102 103 104 1050
1000
2000
3000
4000
5000
0 102 103 104 1050
100
200
300
ROS
Mock DCA
Cells
6526 14146
C
HROS OP9-DL1 cell survival
Mock NAC0
50
100
150
Live
cel
ls (%
)
WT
KO
Mock NAC
Live
cel
ls (%
)
WTRptor–/–
0.0
0.5
1.0
1.5 nsns
Tota
l cel
ls (×
106 )
0
10
20
30Mock Gal/GAO
0 102 103 104 105
0
102
103
104
105
77.4
2.15
0 102 103 104 105
0
102
103
104
105
0.624
20.3
0
5
10
15
20
E
0 102 103 104 105
0
102
103
104
105
50.6
3.88
0 102 103 104 105
0
102
103
104
105
24.9
8.04
0 102 103 104 105
0
102
103
104
105
3.5
17.7
CD4
TCRgd
Mock DCA Gal/GAOD
TCRgd
+(%
)
Mock
NACGSH
Mock
DCAGal/
GAO
CD4
TCRgd
TCRgd
+(%
)
Mock Gal/GAO
**
0
1
2
3
4
5F
Mock
NACGSH
TCRgd
+(×
103 ) **
* G
0
1
2
3
Mock
NACGSH
CD73
MFI
(×10
4 ) ***
CD4+
(%)
MockGal/GAO
**
0 102 103 104 105
0
102
103
104
105
46.8
3.49
0 102 103 104 105
0
102
103
104
105
2.38
6.43
DCAMock
TCRgd
CD4
0
5
10
15
20
TCRgd+
(%)
Mock
NACGSH
***
0
2
4
6
8
10
TCR
gd (x
103)
TCRgd
+(×
103 )
MockGal/GAO
*
CD4+
(×10
4 )
0
5
10
15
MockGal/GAO
***
Fold
cha
nge
ofTC
Rgd
+ /CD4
+
Mock DCA0
10
20
30
40 **
02040
8060
100
012
43
5
0
5
10
15
0
10
20
30
0
5
10
15
20
0
5
10
15
20
0
10
20
40
30
024
86
10
0
50
100
150
0
1
2
3
0
0.5
1.0
1.5
21
Fig. S11
Fig. S11. mTORC1 activation integrates signals from pre-TCR and NOTCH. (A) Plots of p-S6 in DN3 cells from WT and Rag1–/– mice, with MFI plotted in graphs. (B) Phosphorylation of S6 and 4E-BP1 in WT and Rag1–/– DN3a cells co-cultured with OP9-DL1 cells in the absence of IL-7 for 2 days (WT, n = 4; Rag1–/–, n = 5). (C and D) DN3a cells from WT or Bcl2-Tg mice were cultured with OP9 or OP9-DL1 cells in the absence of IL-7 for 2 days, followed by examination of phosphorylation of S6 and 4E-BP1 (C) and Annexin V and 7-AAD (D), with MFI plotted above graphs. (E) WT DN3 cells were co-cultured with OP9-DL1 cells in the absence or presence of rapamycin (10 ng/ml) for 3 days, followed by analysis of CD4 and TCRgd. (F) WT DN3 cells were co-cultured with OP9 or OP9-DL1 cells in the absence or presence of IL-7 for 2 days, followed by analysis of p-S6, with MFI plotted in graphs. Data are means ± SEM. **P < 0.005 and ***P < 0.001. Two-tailed unpaired t test (B). Data are representative of three (A, B, E, F), or two (C, D) independent experiments. Numbers indicate percentage of cells in gates.
0 102 103 104 105
0
102
103
104
105
4.19
54.5
0 102 103 104 105
0
102
103
104
105
8.78
34
CD4
TCRgd
Mock
Rapamycin
CA
0 102 103 104 1050
20
40
60
80 34473447
0 102 103 104 1050
100
200
300
400
10071007
p-S6
Cells
WT Rag1–/–
E OP9 OP9-DL1
IL-7 0 102 103 104 1050
20
40
60
80
100
1027
0 102 103 104 1050
20
40
60
80
100 1190
0 102 103 104 1050
10
20
30
40
50
2368
0 102 103 104 1050
20
40
60
80 2556
p-S6
Cells
-
+
FOP9 OP9-DL1
0 102 103 104 105
0
102
103
104
105
5.97
0 102 103 104 105
0
102
103
104
105
1.69
0 102 103 104 105
0
102
103
104
105
3.35
0 102 103 104 105
0
102
103
104
105
3.13
Annexin V
7-AA
D
WT
Bcl2-Tg
OP9OP9-DL1
Bcl2-Tg
p-4E-BP10 102 103 104 105
0
20
40
60
80
100
1014/2564
p-S60 102 103 104 105
0
20
40
60
80
100
982/6457
WT
0 102 103 104 1050
20
40
60
80
100
981/2424
0 102 103 104 1050
20
40
60
80
100
742/6741
p-4E-BP1p-S6
D
B
0
1
2
3
4
0
5
10
15
p-S6
MFI
(×10
3 )
p-4E
-BP1
MFI
(×10
3 )
WT
Rag1 ko
WTWT
Rag1 koRag1–/–
*****
22
Fig. S12
D
GmTORC1 signaling
tSNE-1
tSNE
-2
High
Low
Glycolysis
tSNE-1
tSNE
-2
-20 -10 10 200
-20
-10
1020
0
MYC targets
-20 -10 10 200
-20
-10
1020
0
tSNE-1-20 -10 10 200
tSNE
-2-2
0-1
010
200
A B
NES = 1.57FDR = 0.027
NES = 1.88FDR = 0.003
NES = 1.77FDR = 0.004
C
p-p65 b-ACTIN
0
Rptor–/–WT
45 75 0 45 75Time (s)
NOTCH signaling
tSNE-1-20 -10 10 200
-20
-10
1020
0tS
NE-2
High
Low
F
Cluster1 5 8 2 4 7 3 6
***
Il2ra
Ptcra
***
***
E
***Notch1
Notch3
Dtx1
1 5 8 2 4 7 3 6Cluster
Dtx3l
***
***
***
tSNE-1
Pre-TCR signaling
-20 -10 10 200
-20
-10
1020
0tS
NE-2
ERK signaling
tSNE-1
tSNE
-2-2
0-1
010
200
-20 -10 10 200
High
Low
H
1 5 8 2 4 7 3 6Cluster
Slc7a5
Shmt2***
***
Ldha
Tpi1***
***
23
Fig. S12. RAPTOR deficiency alters transcriptional programs and signaling and metabolic pathways. (A and B) GSEA reveals the significant enrichment of the upregulated pathways in RAPTOR-deficient DN3a cells (A) and gd T cells (B). (C) Immunoblot analysis of p-p65 (S276) and b-ACTIN in WT and RAPTOR-deficient gd T cells stimulated with anti-gdTCR for the indicated time points. (D) Violin plots of Il2ra, Ptcra and additional NOTCH target genes among the 8 clusters. A violin plot combines the box plot and the local density estimation into a single display. The black bars and thin lines within the violin plots indicate the interquartile range (1st quantile – 3rd quantile) and the entire range of the data (up to 1.5 fold of interquartile range from 1st/3rd quantile), respectively, and the white dots in the center indicate the median values. (E) tSNE (top) and violin (bottom) plots of the signature of NOTCH signaling among the 8 clusters. (F) tSNE plots of the signatures of pre-TCR and ERK signaling among the 8 clusters. (G) tSNE plots of gene signatures of mTORC1 signaling, MYC targets and glycolysis among the 8 clusters. (H) Violin plots of metabolic genes among the 8 clusters. Data are means ± SEM. ***P < 0.001. Two-tailed Mann-Whitney test (D, E, and H). Data are from one experiment (A, B (WT, n = 3; Rptor–/–, n = 3); D to H (WT, n = 2; Rptor–/–, n = 3)), or representative of two (C) independent experiments.
24
Fig. S13
Fig. S13. Monocle ‘pseudotime’ trajectory of single cell transcriptomics data. (A) Top left panel: pseudotime trajectory of scRNA-seq data colorized by pseudotime (blue indicates early time, and red indicates late time). The remaining 8 panels are the cell projections for each individual cluster. (B) Projection of DN3a gene signature onto pseudotime trajectory. (C) Projection of gd T cell gene signature onto pseudotime trajectory. (D) Pseudotime trajectory of cellular density colorized by genotypes: WT (blue) and Rptor–/– (red).
Late (Cluster 4)
Cluster 2
Early (Clusters 1, 5, 8)
A
D
C
BPseudotime Cluster 1 Cluster 2
Cluster 3 Cluster 4 Cluster 5
Cluster 6 Cluster 7 Cluster 8
DN3a signature
Component 1
Com
pone
nt 2
gd T cell signature
Component 1
Com
pone
nt 2
WT Rptor–/–
Component 1
Com
pone
nt 2
Com
pone
nt 2
Component 1
High
Low
High
Low
High
Low
25
Fig. S14
Fig. S14. Loss of RAPTOR or MYC enhances signal strength. (A and B) Plots of p-ERK (A) and expression of ID3 (B) in WT and Myc–/– DN3 cells, with MFI plotted in graphs. (C and D) Comparison of ROS production (C) and CD5 expression (D) between DN3 cell subsets from WT and Myc–/– mice. (E) WT DN3a cells were co-cultured with OP9-DL1 cells in the absence or presence of DCA (10 mM) for 3 days, followed by analysis of ID3 expression, with MFI plotted in graphs. (F and G) Expression of CD73 on TCRgd+ cells from co-culture of WT DN3a cells with OP9-DL1 cells upon DCA (F) or Gal/GAO treatment (G), with MFI plotted in graphs. Data are representative of at least three independent experiments (A to G).
ACe
lls
p-ERK
WT Myc–/–
0 102 103 104 1050
30
60
90
120
503
0 102 103 104 1050
50
100
150
200
250 1078503 1078
E
0 102 103 104 1050
100
200
300 22827
ID3
Cells
Mock DCA
0 102 103 104 1050
20
40
60
80
3344922827 33449
F
0 102 103 104 1050
5
10
15
206393
0 102 103 104 1050
5
10
15 18064
Mock DCA
CD73
Cells
180646393
G
CD730 102 103 104 105
0
5
10
15
203846
0 102 103 104 1050
20
40
60
35610
Mock Gal/GAO
Cells
3846 35610
D
WTMyc–/–
DN3a DN3b
0 102 103 104 1050
20
40
60
80
100
0 102 103 104 1050
20
40
60
80
100
CD5Ce
lls
ID3
Cells
0 102 103 104 1050
50
100
150
2001094
0 102 103 104 1050
200
400
6001866
Myc–/–WT
18681094
B
C
ROS0 102 103 104 105
0
20
40
60
80
100
0 102 103 104 1050
20
40
60
80
100
Cells
DN3a DN3b 2181/4279 5552/8781WTMyc–/–
26
Fig. S15
Fig. S15. Schematics of mTORC1 and metabolic control of redox homeostasis and signal strength in T cell lineage choices. mTORC1, activated by TCR and NOTCH signaling, intersects with MYC in a feed-forward manner to orchestrate cell metabolism in developing thymocytes. Dynamic metabolic remodeling, including proper engagement of glycolysis and OXPHOS, contributes to anabolism and cell expansion. However, dysregulated metabolism results in aberrant ROS production and altered redox balance, which in turn impinges upon the signal strength, thereby altering lineage choices of ab and gd T cells.
RAPTOR/mTORC1
MYC
Metabolic remodeling(Glycolysis & OXPHOS)
IL-7
NOTCH (DN3a) TCR
(DN3b)
⍺b T cells gd T cellsLineage choices
AnabolismSignal strength
ROS