A CRISPRi/a screening platform to study cellular nutrient transport in diverse microenvironments

Abstract

Blocking the import of nutrients essential for cancer cell proliferation represents a therapeutic opportunity, but it is unclear which transporters to target. Here we report a CRISPR interference/activation screening platform to systematically interrogate the contribution of nutrient transporters to support cancer cell proliferation in environments ranging from standard culture media to tumours. We applied this platform to identify the transporters of amino acids in leukaemia cells and found that amino acid transport involves high bidirectional flux dependent on the microenvironment composition. While investigating the role of transporters in cystine starved cells, we uncovered a role for serotonin uptake in preventing ferroptosis. Finally, we identified transporters essential for cell proliferation in subcutaneous tumours and found that levels of glucose and amino acids can restrain proliferation in that environment. This study establishes a framework for systematically identifying critical cellular nutrient transporters, characterizing their function and exploring how the tumour microenvironment impacts cancer metabolism.

Fig. 1. CRISPRi/a screens identify the transporters of amino acids in K562 cells.

a, A cartoon of the general approach used to identify transporters in cells. Individual transporter genes are knocked down via CRISPRi or overexpressed via CRISPRa, and modified transport activity is detected by changes in proliferation. b, CRISPRi/a of transporters leads to specific changes in gene expression. Expression levels in K562 CRISPRi/a cells with specific or non-targeting control (NTC) sgRNAs were quantified by RT–qPCR relative to the housekeeping gene GAPDH. Data are mean ± s.e.m of n = 3 technical replicates. The horizontal dashed lines represent the average of both NTCs. n.d., not detected. c, CRISPRi/a of transporters leads to changes in protein level at the plasma membrane. K562 CRISPRi/a cells were incubated with a cell-impermeable biotinylation reagent, and plasma membrane proteins were isolated by streptavidin affinity purification and analysed by western blotting. d, The identification of amino acids that limit proliferation of K562 cells when their level in growth medium is reduced. Data were determined using a luminescent cell viability assay and represent the average log₂FC relative to time of 0 days (T0) of n = 4 biologically independent samples. e, A cartoon of the pooled screening strategy used to identify amino acid transporters. Library pools were grown in RPMI media where specific amino acids were present at a level that reduced proliferation by 50% relative to complete RPMI (low amino acid). a.a., amino acid. f, Volcano plots of transporter CRISPRi/a screens in K562 cells in low lysine and low arginine. Black circles represent transporter genes, and red circles represent negative control genes. n = 2 screen replicates. g, Bubble plots displaying CRISPRi screen scores determined for all 64 transporters annotated as capable of amino acid transport. h, Same as g for CRISPRa screens. In f–h, the phenotype scores represent averaged and normalized sgRNA enrichments in low amino acid versus RPMI, and −log₁₀(P value) was determined using a Mann–Whitney test of sgRNA enrichments compared with all NTC sgRNAs. Source numerical data and unprocessed blots are available in Source data. Source data

Fig. 2. Measurement of amino acid transport rates in K562 cells reveals that CRISPRi of SLC7A5 specifically reduces transport of large neutral amino acids.

a, The phenotype scores obtained in transporter CRISPRi/a screens for SLC7 family genes were validated in competition assays in K562 cells in RPMI and in RPMI with amino acids adjusted to human plasma levels (PAA-RPMI). b, The import of amino acids into K562 cells in RPMI was determined by quantifying the intracellular accumulation of stable heavy-isotope-labelled amino acids over time by GC–MS. One example representative of six independent experiments. c, Amino acid import and cellular consumption rates of K562 cells growing in RPMI. Import rates were determined by linear regression of the early phase of heavy-isotope-labelled amino acid accumulation. Consumption rates were determined by linear regression of amino acid levels in the growth medium of K562 cells over time. n = 6 biologically independent samples for import and n = 5 for consumption. Data are mean ± s.e.m. d, Intracellular amino acid levels (n = 7 biologically independent samples) and pool turnover rates of K562 cells growing in RPMI. Pool turnover rates were inferred by dividing amino acid import rates in c by intracellular levels. Data are mean ± s.e.m. e, Screen scores for K562 SLC7A5 CRISPRi/a. f, Amino acid import rates for K562 SLC7A5 and non-targeting control (NTC) CRISPRi in RPMI and in low leucine. Data represent the slope ± SE determined from the linear regression of n = 7 biologically independent samples. g, Cellular consumption rates for K562 SLC7A5 and NTC CRISPRi in RPMI. h, Consumption of leucine from low-leucine medium by K562 SLC7A5 and NTC CRISPRi cells. In g and h, the data represent the slope ± SE determined from the linear regression of n = 6 biologically independent samples. The shaded area represents 95% confidence interval. i, Intracellular amino acid levels of K562 SLC7A5 and NTC CRISPRi cells growing in RPMI or in low leucine (n = 8 biologically independent samples; data are mean ± s.e.m.). Source numerical data are available in Source data. Source data

Fig. 3. SLC43A1/LAT3 is a net exporter of large neutral amino acids.

a, Scores obtained in CRISPRi/a screens in low amino acid and in RPMI. b, SLC7A1 CRISPRi/a and SLC7A7 CRISPRa specifically alter the import of arginine and lysine in K562 cells in RPMI. c, SLC7A7 CRISPRa increases the import of lysine when lysine is present in the medium at growth-limiting concentrations. SLC7A6 CRISPRa increases the import of arginine and lysine in all conditions tested. The arrows highlight the import of Arg and Lys in RPMI, import of Lys in low-Lys conditions and import of Arg in low-Arg conditions. d, SLC38A3 CRISPRa increases import of histidine and glutamine into K562 cells in RPMI. e, CRISPRa of SLC43A1 and SLC43A2 induces a proliferation defect in K562 over a range of conditions (RPMI with regular FBS, RPMI with dialyzed FBS (dFBS), and RPMI modified such that amino acids match human plasma levels (PAA-RPMI)). Data are the mean ± s.e.m. of two biological replicates each with six technical replicates. f, CRISPRa of SLC43A1 increases import of isoleucine and valine into K562 cells. g, CRISPRa of SLC43A1 decreases intracellular levels of large neutral amino acids in K562 cells. Levels were determined from import assays in f (n = 7 biologically independent samples; data are mean ± s.e.m.). h, CRISPRa of SLC43A1 increases the export rate of valine from K562 cells cultured in RPMI. In b–d,f and h, the rates were determined from a linear regression of n = 6 biologically independent samples. The data represent the slope ± SE normalized to a non-targeting control (NTC). Source numerical data are available in Source data. Source data

Fig. 4. Serotonin imported by SLC6A4 protects cells from ferroptosis via endogenous antioxidant activity.

a, Scores obtained in transporter CRISPRi/a screens in K562 cells in low-cystine medium. b, SLC6A4 CRISPRa provides a growth advantage in low cystine dependent on SLC6A4 activity. Phenotype scores were determined in competition assays in low-cystine medium replicating screen conditions and represent the average log₂FC between a specific and non-targeting control (NTC) sgRNA. Fluoxetine, 10 μM; serotonin, 1 μM; 5-OH Trp, 1 μM. n = 3 biologically independent samples. c, SLC6A4 CRISPRa provides resistance to K562 cells over a range of cystine levels. Cell viability was quantified in a luminescent assay after 48 h in low cystine. n = 4 biologically independent samples. d, Serotonin protects K562 cells from death in low cystine and expression of SLC6A4 increases protection. Assay as in c, and viability was normalized to the initial cell count (T0). Fluoxetine, 5 μM; serotonin: 1 (+) or 10 (++) μM; 5-OH Trp, 1 (+) or 10 (++) μM. n = 6 biologically independent samples. e, Serotonin protects K562 cells from death independent of GPX4 activity. Assay as in d. Ferrostatin-1, 0.1 μM; RSL3, 1 μM; erastin, 5 μM. f, Serotonin protects A375 cells from ferroptosis only at high concentration. Assay as in d. n = 4 biologically independent samples. g, Addition of serotonin (1 (+) or 10 (++) μM) reduces lipid peroxide levels in K562 cells induced for ferroptosis, dependent on SLC6A4 expression. Each data point represents the average of 20,000 cells determined by flow cytometry. n = 3 biologically independent samples. Peroxide levels were normalized to the ‘+Cys’ condition represented by the horizontal dashed line (normalized to a value of 1). h, Serotonin protects Caco-2 cells from ferroptosis dependent on SLC6A4 expression and independent of GPX4 activity. Assay as in d. n = 10 biologically independent samples. i, A cartoon illustrating the role of serotonin and SLC6A4 in suppressing ferroptosis. Boxed areas are from this study; core ferroptosis pathway components and ferroptosis-modulating molecules were adapted from others^,. In d–f and h, P values were determined using two-tailed unpaired Student’s t-tests. In b–h, data are mean ± s.e.m. Source numerical data are available in Source data. Source data

Fig. 5. Transporter essentiality is highly condition specific, which can be leveraged to decipher transporter function.

a, Essential transporters in K562 cells growing in RPMI determined using CRISPRi screening. n = 2 screens each with two technical replicates. P values were determined using a Mann–Whitney test. b, A chord plot displaying all essential transporters from a. c, A tile plot of growth scores determined in CRISPRi screens in K562 cells across culture conditions (n = 2 replicates). All transporters significantly enriched or depleted in at least one condition are included. ‘#’ highlights transporters discussed in the main text. d, A comparison of essentiality between conditions. Data represent growth scores for all transporters as determined in c. e, Growth complementation assays identifying SLC39A10 as the main zinc importer in K562 cells and manganese as a competing ion. Phenotypes were determined in RPMI + FBS, in RPMI + dFBS or in RPMI + dFBS supplemented with 10 μM metal ion. Data are mean ± s.e.m. of three technical replicates for non-targeting control (NTC) and three technical replicates of two biological replicates for SLC39A9 and SLC39A10. f, A cartoon illustrating the role of the mitochondrial pyruvate carrier (MPC1/MPC2) and the major sources and uses of cytoplasmic and mitochondrial pyruvate. Exogenous AKB was used to restore NAD⁺ levels. ETC, electron transport chain. g, The addition of either alanine or of molecules restoring NAD⁺ levels alleviates the defect of MPC1/2 CRISPRi. Phenotypes were determined in competition assays in RPMI + FBS and in RPMI + dFBS with 130 μM alanine, 1 mM AKB, 1 mM pyruvate, 4.5 mM lactate or 130 μM alanine + 4.5 mM lactate. Data are mean ± s.e.m. of two biological replicates each with three technical replicates. Horizontal dashed lines highlight the growth phenotype in the ‘+dFBS’ condition. h, A comparison of transporter essentiality in K562 and A375 cells. Data (n = 2 replicates) are the phenotype scores determined in CRISPRi screens in RPMI for all transporters significantly depleted in at least one cell line. In a and d, black circles indicate transporter genes and red circles indicate negative control genes. In e and g, P values were determined using two-tailed unpaired Student’s t-tests. Source numerical data are available in Source data. Source data

Fig. 6. Identification of essential transporters and environmental growth limitations in subcutaneous tumours.

a,b, Pools of K562 and A375 transporter CRISPRi/a libraries were injected subcutaneously in the flank of immunodeficient mice. Growth scores were determined from enrichment/depletion of sgRNAs in whole tumour homogenates (n = 4). The red circles represent negative control genes. c,d, A comparison of transporter CRISPRi/a phenotypes in subcutaneous (subQ) tumours to phenotypes in growth culture media identifies condition-specific effects. Pools of cells prepared in a and b were cultured in ABS (100% bovine serum), in HPLM and in RPMI. Data represent all transporters significantly enriched or depleted in at least one condition, and growth scores determined from two replicates were normalized to the most depleted transporter in each screen. ‘#’ highlights transporters discussed in the main text. e,f, Pearson correlation coefficients determined from pairwise comparison of growth scores for enrichments/depletions in c and d and P values were determined using a two-sided test. **: P < 1 × 10⁻⁴; ***: P < 1 × 10⁻⁶. In a and b, P values were determined using a Mann–Whitney test. In a, c and e, data are from CRISPRi screens. In b,d and f, data are from CRISPRa screens. Source numerical data are available in Source data. Source data

Extended Data Fig. 1. Transporter CRISPRi/a screening optimization.

(a) Composition of the CRISPRi/a transporter sgRNA libraries. (b) Preparation of K562 CRISPRi or CRISPRa pooled transporter libraries. Custom sgRNA libraries for CRISPRi and CRISPRa were constructed and packaged into lentivirus. A K562 monoclonal cell line expressing either the CRISPRi or CRISPRa machinery was infected with lentiviral particles, and untransduced cells were removed by antibiotic selection. Because transporter expression is often tissue-specific and cell lines can lose transporter expression over time, CRISPRa bypasses the need for transporter expression. CRISPRi was used over CRISPR/Cas9 knockout because genetic loss-of-function strategies are often complicated by transcriptional adaptation^,. (c) Assessment of the activity of the mTORC1 and GCN2 pathways in low amino acid screen-like conditions for Arg, Lys, and His via Western blotting of downstream targets eIF2α (GCN2), and S6 kinase and 4E-BP1 (mTORC1). K562 cells grown in complete RPMI medium (Rich; the triangle depicts a centrifugation step) were rapidly pelleted and transferred to either Rich, low amino acid RPMI, or single amino acid dropout RPMI and samples were taken at specific time points after media exchange. The media were exchanged after 24 h (medium exchange denoted by asterisk). For all samples, cell lysates were immunoblotted with antibodies against vinculin (loading control), and phospho- and total antibodies against eIF2α, S6K, and 4E-BP1. (d) CRISPRi/a of transporters leads to specific changes in gene expression. Transporter expression levels in K562 CRISPRi/a cells with specific or non-targeting control (NTC) sgRNAs and grown in low Arg or low Leu RPMI was quantified by RT-qPCR relative to the housekeeping gene GAPDH. n = 3 technical replicates. Data are mean ± s.e.m. n.d., not detected. (e) low Arg and low Leu conditions induce a small transcriptional upregulation of most tested SLC7 transporters. Data are a subset of expression levels determined in Fig. 1b and (d). For each gene, expression levels of CRISPRi/a cell lines targeting that gene and NTC cell lines were plotted. Source numerical data and unprocessed blots are available in source data. Source data

Extended Data Fig. 2. CRISPRi/a transporter screens in low amino acid conditions.

(a) CRISPRi/a of transporters leads to changes in protein level at the plasma membrane. Changes in low Leu (L) and low Arg (R) conditions are similar to those observed in RPMI (rich). K562 CRISPRi/a cells were incubated with a cell-impermeable biotinylation reagent, and plasma membrane proteins were isolated by streptavidin affinity purification and analyzed by Western blotting. * denotes a non-specific band. (b) Tile plots displaying all significant transporter CRISPRi/a hits in low amino acid screens. Screen scores were calculated by multiplying phenotype scores by -log10(P value) for all genes and computed negative controls. P values were determined using a Mann-Whitney test. A stringent score cutoff was determined by the highest scoring negative control across all conditions for both datasets. All transporter genes that were significant in at least one condition tested were included in the tile plot. Transporter CRISPRi/a cell lines that have a growth defect in RPMI were prone to being pan-resistant in low amino acid conditions, as previously reported in other screens, and were removed from the analysis (see Methods). Columns were ordered based on hierarchical clustering of scores. (c) CRISPRi/a transporter screens are highly reproducible as shown by the strong correlation between independent screen replicates. Black circles represent individual transporter genes and red circles indicate negative control genes. (d) The custom sgRNA library is highly homogenous, and the library preparation introduces no sample bias. gDNA was isolated from a pool of K562 CRISPRi transporter library cells post-antibiotic selection, and the abundance of each sgRNA present in the library was determined after PCR amplification and high-throughput sequencing (Methods). For this representative sample, >97% of sgRNAs were within 1 log2 of the mean, 12 sgRNAs had less than 1000 counts, and no sgRNA had 0 counts. Source numerical data and unprocessed blots are available in source data. Source data

Extended Data Fig. 3. Measurement of amino acid import and consumption rates in K562 cells.

(a) A cartoon illustrating the competition assay used to validate growth phenotypes of CRISPRi/a cell lines. A CRISPRi/a cell line expressing a test sgRNA is mixed at a 1:1 ratio with a cell line expressing a non-targeting control (NTC) sgRNA. Cell line ratios pre- and post-treatment are determined by qPCR on gDNA extracted from cultures using primers specific to the sgRNA. Phenotype scores are determined by normalizing enrichments by the difference in population doublings. (b) Confirmation of the specificity of primers targeting sgRNAs in qPCR assays. gDNA extracted from K562 CRISPRi/a cells was amplified by qPCR with primers targeting specific and NTC sgRNA barcodes. Data are the cycle threshold (Ct) value of a representative experiment. Asterisks represent non-specific product amplification. (c) SLC7A8 expression level in K562 SLC7A8 CRISPRi/a cells determined by RT-qPCR relative to the housekeeping gene GAPDH. n = 3 technical replicates. Data are mean ± s.e.m. (d) Cartoons illustrating amino acid import and consumption rate determination. The medium surrounding K562 cells attached to the surface of a Petri dish is rapidly exchanged to a similar medium where amino acids are heavy-isotope labelled. After extensive washing with PBS, intracellular metabolites are extracted and light and heavy amino acid levels are quantified by GC-MS. Import rates are determined from the slope of the increase in heavy amino acids over time. For consumption rate determination, the medium surrounding K562 cells is exchanged to fresh unlabelled medium and media samples are taken over time. Amino acid levels in samples are determined by GC-MS, and consumption rates are determined by the slope of the change in levels over time. (e) External standard curve used to calculate absolute levels of amino acids. Representative example of two independent experiments (f) Representative example of amino acid consumption from the medium of K562 cells growing in RPMI. Data (n = 7 biologically independent samples) were fit to a linear model and the shaded area represents 95% CI. (g) Numerical values of data displayed in Fig. 2c,d. Source numerical data are available in source data. Source data

Extended Data Fig. 4. SLC7A5 CRISPRi specifically reduces transport of large neutral amino acids.

(a) Comparison of amino acid import rates and inferred export rates in K562 cells growing in RPMI. Import rates are from Fig. 2c, and export rates were calculated by subtracting consumption rates from import rates. (b,c) Additional data related to Fig. 2f,i for amino acids that are not SLC7A5 substrates. (d) Comparison of amino acid import rates in low leucine RPMI and in RPMI for K562 SLC7A5 and non-targeting control (NTC) CRISPRi determined in Fig. 2f. (e) Comparison of intracellular amino acid levels for K562 SLC7A5 and NTC CRISPRi in low leucine RPMI and in RPMI determined in Fig. 2i. (d,e) Asterisks indicate SLC7A5 substrates. Source numerical data are available in source data. Source data

Extended Data Fig. 5. Specific changes to amino acid levels and transport rates induced by CRISPRi/a of SLCs.

(a) Changes in intracellular amino acid levels induced by CRISPRi/a of SLC7A1 and SLC7A7. (b) Same as (a) but for SLC38A3 CRISPRa and data from Fig. 3d. (c) LAT1–4 expression levels in K562 CRISPRi/a cell lines were determined by RT-qPCR relative to the housekeeping gene GAPDH. n = 3 technical replicates. Data are mean ± s.e.m. (d) Amino acid import rates into K562 cells correlate with levels of amino acid in the growth medium. Import rates for K562 CRISPRa SLC43A1 and non-targeting control (NTC) in RPMI and PAA–RPMI were from Fig. 3f. Data represent the ratio of import rates in PAA–RPMI to that in RPMI. Relative amino acid levels in the media were determined from their respective formulation. (e) The import of amino acids into K562 cells in low amino acid medium is selectively diminished for that specific low abundance amino acid. Data: mean ± s.e.m. of n = 2 (low Val), n = 3 (low Leu), n = 4 (RPMI) independent import rate determinations each calculated from the linear regression of n = 6 biologically independent samples. (f) SLC43A2 CRISPRa increases import of isoleucine and valine into K562 cells in RPMI. Rates were determined from a linear regression of n = 6 biologically independent samples. Data represent the slope ± SE normalized to NTC. Data for SLC43A1 CRISPRa is from Fig. 3f. (g) SLC43A2 CRISPRa induces a decrease in intracellular levels of large neutral amino acids in K562 cells grown in RPMI. (h) SLC43A1 CRISPRa leads to higher export of valine and similar export of leucine, isoleucine, and phenylalanine despite lower intracellular pools. Cells grown in RPMI containing heavy-isotope labelled amino acids were rapidly washed, then incubated in regular RPMI. Accumulation of heavy-labelled amino acids in RPMI was monitored over time by GC-MS. Representative example of 3 independent experiments. (a,b,d,g) n = 7 biologically independent samples. Data are mean ± s.e.m. Source numerical data are available in source data. Source data

Extended Data Fig. 6. Specific changes to expression level of SLCs by CRISPRi/a.

(a) RT-qPCR analysis shows strong and specific gene upregulation by CRISPRa. n = 3 technical replicates. Data are mean ± s.e.m. (b) SLC1A2 CRISPRa confers a growth advantage in low and no glutamine RPMI in the presence or absence of either glutamate or aspartate in the medium. Proliferation rates were extracted from competition assays with 4 biologically independent samples from two independent sgRNAs. P values were determined using two-tailed unpaired Student’s t-tests. (c) Expression level of SLC6A4 in cell lines used in this study and specific up- and down-regulation of SLC6A4 via CRISPRi/a. Levels of SLC6A4 and SLC7A11 were quantified by RT-qPCR relative to the housekeeping gene GAPDH in K562 CRISPRa, A375 CRISPRa, and Caco-2 CRISPRi cell lines with sgRNAs targeting SLC6A4, SLC7A11 or a non-targeting control (NTC). n = 3 technical replicates, except for A375 and Caco-2 NTC where n = 6. Data are mean ± s.e.m. (d) Violin plots displaying the distribution of lipid peroxidation levels in single cells (~20k) as determined by flow cytometry of cells incubated with BODIPY 581/591 C11 sensor after growth in the mentioned conditions. Red bars represent the average peroxidation level of the population and were used in Fig. 4g. (e) Low cystine induces expression of SLC7A11 but not of SLC6A4. K562 and Caco-2 cells were grown in either RPMI or low cystine RPMI for 3 days with daily media changes. Expression levels were quantified by RT-qPCR relative to GAPDH (n = 3 technical replicates. Data are mean ± s.e.m.). Source numerical data are available in source data. Source data

Extended Data Fig. 7. Transporter essentiality across conditions.

(a) CRISPRi transporter screens are highly reproducible as shown by the strong correlation of screen scores between independent replicates. Black circles represent individual transporter genes and red circles indicate computed negative control pseudogenes assembled from non-targeting control (NTC) sgRNAs. (b) Comparison of essential transporter genes identified in this study to previous screens in K562 cells grown in RPMI. The strong enrichment in expressed genes (98%) amongst the essential transporters in this study highlights the quality of the dataset. Essential transporters were determined by using a significance cutoff of q-value < 0.05 (ref. ), and p < 0.05 and CS score negative for the two CRISPRko screens (KO: knockout), and the first negative control gene for the genome-wide CRISPRi screen (KD: knockdown). Genes were binned by expression level using publicly available data. (c) Pairwise comparison of transporter growth scores determined in this study and in a genome-wide CRISPRi screen in the same cell line, medium, and using the same gRNA library. (d,e) Transporter expression levels in K562 CRISPRi cell lines determined by RT-qPCR and relative to the housekeeping gene GAPDH (n = 3 technical replicates. Data are mean ± s.e.m.). (f) Growth scores of K562 MPC1 and MPC2 CRISPRi determined in screens in different media and the concentration of a selection of metabolites in those media. Screen scores are from Figs. 5c, 6c and metabolite levels are from published data. (g) Addition of pyruvate to RPMI + FBS or RPMI + dialyzed FBS (dFBS) alleviates the growth defect induced by CRISPRi of MPC1 or MPC2, and addition of lactate to RPMI + dFBS worsens the growth defect. Assays were performed as in Fig. 5g and data were normalized to untreated samples. n = 6 biologically independent samples. Data are mean ± s.e.m. P values were determined using two-tailed unpaired Student’s t-tests. (h) Expression level of SFXN1 in K562 and A375 CRISPRi cell lines determined by RT-qPCR and relative to GAPDH (n = 3 technical replicates. Data are mean ± s.e.m.). Source numerical data are available in source data. Source data

Extended Data Fig. 8. Optimization of subcutaneous CRISPRi/a transporter screens.

(a) Determination of the engraftment frequency of K562 cells injected subcutaneously into immunodeficient mice. K562 CRISPRi library cells expressing GFP were mixed into pools of K562 CRISPRi library cells at a ratio ranging from 1:1000–1:100,000. 10, 1 or 0.1 million cells of these preparations were injected into the flanks of mice. Tumors formed over 19–34 days, were harvested and dissociated into single cell suspensions. The presence (or absence) of GFP⁺ cells in homogenized tumor samples was determined by flow cytometry and was used to determine the engraftment frequency and conditions required to preserve library complexity during in vivo transporter screens. (b) Plot displaying the number of counts for all non-targeting control (NTC) sgRNAs determined either from the sequencing of tumor samples from (a), from the initial pool of library cells, or from library cells grown in RPMI. (c) Expression level of SLC2A1 in K562 and A375 CRISPRa cell lines determined by RT-qPCR and relative to the housekeeping gene GAPDH. n = 3 technical replicates. Data are mean ± s.e.m. (d) Overexpression of SLC2A1 via CRISPRa leads to increased levels at the plasma membrane in K562 and A375 cells. Cells were incubated with a cell-impermeable biotinylation reagent, and plasma membrane proteins were isolated by streptavidin affinity purification and analyzed by Western blotting. (e) Example of gating strategy used for flow cytometry sorting of K562 cells transduced with lentivirus expressing dCas9-BFP-KRAB for K562 CRISPRi/a cell line generation. Source numerical data and unprocessed blots are available in source data. Source data