Dendritic cells (DCs) regulate processes ranging from antitumor and antiviral immunity to host-microbe communication at mucosal surfaces. It remains difficult, however, to genetically manipulate human DCs, limiting our ability to probe how DCs elicit specific immune responses. Here, we develop a CRISPR-Cas9 genome editing method for human monocyte-derived DCs (moDCs) that mediates knockouts with a median efficiency of >94% across >300 genes. Using this method, we perform genetic screens in moDCs, identifying mechanisms by which DCs tune responses to lipopolysaccharides from the human microbiome. In addition, we reveal donor-specific responses to lipopolysaccharides, underscoring the importance of assessing immune phenotypes in donor-derived cells, and identify candidate genes that control this specificity, highlighting the potential of our method to pinpoint determinants of inter-individual variation in immunity. Our work sets the stage for a systematic dissection of the immune signaling at the host-microbiome interface and for targeted engineering of DCs for neoantigen vaccination. Dendritic cells (DCs) play an outsized role in orchestrating innate and adaptive immunity: they act as sentinels, detecting invaders and initiating innate immune responses to clear them, and as antigen-presenting cells, initiating adaptive immune responses that are antigen-specific and tailored to the context in which the antigen was detected . In thisDissecting the pathways by which human DCs respond to innate immune stimuli and relay them into adaptive responses, however, has been challenging, due in large part to difficulties in genetically manipulating human DCs. Although approaches for gene repression in human DCs by RNAi have been reported , RNAi suBacteroides thetaiotaomicron (B. theta), recapitulating known LPS signaling pathways and revealing candidate genes that mediate species-specific LPS recognition and give rise to inter-individual variation in the response to LPS. These results highlight the potential of our strategy for identifying receptors for innate immune ligands, such as those from the human microbiome, and for pinpointing the genetic bases of inter-individual variation in human immunity. More broadly, our work now provides a general blueprint for functional genomics in human DCs.To address these limitations, we developed a CRISPR-Cas9 strategy to construct targeted knockouts directly in human monocyte-derived DCs (moDCs), which are readily derived from donor blood and are widely used for research and clinical applications . Using tTo enable introduction of specific knockouts in human moDCs, we developed a non-viral genome editing strategy based on electroporation of in vitro-assembled Cas9-sgRNA complexes , an approach that has been validated in other immune cell types . BrieflyAAVS1, using a validated sgRNA sequence  after knockout of TNF or the LPS receptor TLR4  leveraged sgRNA design tools optimized for RNP activity  and (ii) targeted each locus with two to three sgRNAs with binding sites tiled across a 200 bp stretch to induce simultaneous double-strand breaks, a design that increases the likelihood of achieving functional knockouts by preventing error-free DNA repair and/or removing a stretch of the gene  . Becausetor TLR4  but not ignaling . We usedignaling . Benchmaignaling .E. coli LPS, a TLR4 agonist, and measured production of two cytokines: (i) TNF-α, which is induced by MYD88 and TRIF (TICAM1) downstream of TLR4, and (ii) CXCL10 (IP-10), which is induced by TRIF via activation of IRF3 and production of interferon beta (IFNB1), independently of MYD88 , TICAM1 knockout strongly reduced CXCL10 production and moderately reduced TNF-α production, and knockout of IFNB1 or IRF3 only reduced CXCL10 production  that had been cultured alongside the knockout moDCs in 96-well plates  We purified LPS from a B. theta strain carrying deletions of all eight capsular polysaccharide (CPS) biosynthetic gene clusters (B. theta WT LPS (ΔCPS) or just B. theta LPS. Human moDCs stimulated with B. theta LPS secreted moderate levels of TNF-α as quantified by ELISA; this response was weaker than that elicited by E. coli LPS both in magnitude and apparent EC50 but substantially stronger than that elicited by Rhodobacter sphaeroides LPS, a well-characterized TLR4 antagonist  Although the pattern was consistent across moDCs from independent donors, response magnitude and EC50 varied by sixfold and >20-fold, respectively   Thus, B. theta LPS is a partial TLR4 agonist whose immunostimulatory activity can be tuned by rational engineering.To further establish if bacteria , in addi pathway . Hypotheagonists  and thus4PP LPS) . B. thete donors .  to treatment with PBS (mock), B. theta WT LPS, or E. coli O55 LPS by RNA-seq (RPE65 from two independent donors from this experiment (donors p and q) to the profiles of unedited moDCs from a previous experiment (donors r and s). Transcriptional profiles segregated primarily by donor, and each of the profiles of knockout moDCs more closely resembled those of unedited moDCs from one donor than the profiles of unedited moDCs from different donors resembled each other . For each donor, we observed altered TNF-α secretion for all four wells targeting the positive control gene TNF and one well targeting a neutral control gene (of 36 such wells).To identify additional factors that contribute to recognition of c screen . We desiTNF) impacted TNF-α secretion in response to B. theta WT LPS in both donors. Knockouts in the TLR4 signaling pathway strongly reduced TNF-α secretion, including TLR4 and its co-receptor MD2 (LY96), CD14 (which delivers LPS to TLR4 and initiates TLR4 endocytosis [RELA (TNFAIP3), which inhibits LPS- and TNF-α-induced NF-κB signaling, secreted more TNF-α (B. theta LPS.Knockout of 15 genes (including ocytosis ), as welis [RELA . Indeed,is [RELA . Other kre TNF-α . Thus, oPTPN6 (SHP-1) and to a lesser extent IL10 increased TNF-α secretion in response to B. theta WT LPS in one donor (‘donor i’) but not the other (‘donor h’), suggesting that these factors can suppress TNF-α secretion in response to LPS stimulation in a manner that differs among individuals , and TRIF (TICAM1)  . These d(TICAM1) . A separ(TICAM1) .TIRAP knockout caused a larger decrease in TNF-α secretion than TLR4 knockout in donor h, we wondered if other TLRs contributed to the response to B. theta WT LPS. We focused on TLR2, canonically known as a receptor for lipopeptides and teichoic acids, because TLR2 knockout caused the next-strongest decrease in TNF-α secretion among TLRs and because TLR2 has been implicated in the response to non-proteobacterial LPSs  with 10% (v/v) standard fetal bovine serum , 100 units/mL penicillin, 100 µg/mL streptomycin, and 2 mM l-glutamine (Gibco). Lyophilized recombinant human GM-CSF  and recombinant human IL-4 (Gemini Bio) were reconstituted to 100 µg/mL and 40 µg/mL, respectively, in sterile ddH2O, aliquoted into 40–100 µL aliquots, and frozen at –30°C until use. Fluorescently labeled antibodies against human CD14 , CD80 , CD83 , CD86 , HLA-DR , CD11b , CD11c , CD205/DEC205 , B2M , and TLR4  were obtained from BioLegend . Ghost Dye Violet 510 was obtained from Tonbo Biosciences . Ultrapure LPS from E. coli O55:B5 and Rhodobacter sphaeroides, along with Pam3CSK4, were obtained from Invivogen . Solid medium used for bacterial growth was BHI/blood agar, made from Brain Heart Infusion Agar  with 10% defibrinated horse blood . Liquid medium used for bacterial growth was supplemented BHI broth, made by preparing 1 L Brain Heart Infusion Broth (BD), and immediately before starting cultures adding 1 mL bovine hemin stock , 5 mg/mL in 1 N sodium hydroxide and filter sterilized, and 10 mL l-cysteine hydrochloride (Sigma), 50 mg/mL in Milli-Q water and filter sterilized. Sources of sgRNAs and Cas9 are listed below.Complete RPMI medium was generated by supplementing RPMI 1640 medium containing 25 mM HEPES, 2 mM B. theta strains were stored at –80°C in growth medium mixed in equal volume with 50% glycerol in water. Strains were streaked from glycerol stocks onto BHI/blood agar using plastic inoculating loops. Strains were allowed to grow 24–48 hr in an anaerobic chamber. Single colonies were used to inoculate 4 10 mL aliquots of supplemented BHI broth per strain, and after 24 hr the 10 mL cultures were expanded to 1 L each in glass bottles, producing 4 L total culture volume per strain. Cultures were allowed to grow to stationary phase (24–36 hr) and were pelleted at 3400 × g for 1 hr at 4°C. Pellets were washed in PBS and shipped frozen to the UCSD GlycoAnalytics Core for LPS purification.B. theta strain (ΔCPS) and the B. theta 4PP strain have been previously reported , and the 4PP strain has had only lipid A acyltransferase BT2152 and lipid A phosphatase BT1854 deleted, not the CPS gene clusters.Both the acapsular reported . BrieflyB. theta LPS preparations were performed by Biswa P. Choudhury at the UCSD GlycoAnalytics Core. A cell pellet from 4 L confluent culture of each B. theta strain was suspended in Milli-Q water and mixed with an equal volume of 90% phenol solution . The suspension was stirred continuously and maintained at 68°C ± 2°C for 30 min. After cooling in an ice bath, suspensions were centrifuged at 3500 rpm at 10°C for 45 min and the upper layer removed to a clean Falcon tube. The remaining layers were extracted again with an equal volume of water for 30 min, cooled, and centrifuged as before. The upper layers were pooled and dialyzed  against 4 L of water for 4 days, replacing the water twice per day. The dialysate was lyophilized, resuspended in water, and subjected to ultracentrifugation at 105,000 × g for 4 hr. The pellet was resuspended in water, treated with DNase I, RNase A, and proteinase K, followed by another round of ultracentrifugation as above. The resulting pellet was resuspended in water and lyophilized.6 to 1.3 × 106 per mL at 37°C and 5% CO2 for 7 days. Medium was exchanged every 2 or 3 days during this period . moDCs on day 7 were generally positive for CD80, CD83, HLA-DR, CD11b, CD11c, and CD205 and expressed intermediate levels of CD86 and low to intermediate levels of CD14 with some donor-to-donor variation, as assessed by flow cytometry on an LSR-II flow cytometer (BD BioSciences) or an Attune NxT flow cytometer (Thermo Fisher Scientific). All manipulations were performed in polystyrene conical tubes.Human moDCs were differentiated from monocytes isolated from commercially sourced fresh peripheral blood mononuclear cells (PBMCs) from de-identified healthy human donors . The authors did not obtain identifiable private information on donors. The commercial vendor obtained informed consent from all donors covering all experiments and data reported in this manuscript. Monocytes were isolated from PBMCs by negative magnetic selection using the EasySep human monocyte enrichment kit without CD16 depletion (StemCell) following the manufacturer’s instructions and using a Big Easy magnet or Easy 50 magnet . Enriched monocytes were generally >80% CD14-positive, as assessed by flow cytometry on an LSR-II flow cytometer (BD BioSciences) or an Attune NxT flow cytometer . Cell counts were determined in duplicate using a Countess II automated hemocytometer (Thermo Fisher Scientific). The isolated monocytes were cultured in complete RPMI medium, supplemented with 50 ng/mL GM-CSF and 20 ng/mL IL-4 immediately prior to use, at a density of 1 × 10l-glutamine, with equivalent results. RNA-seq data from moDCs from the same donor differentiated in parallel with and without penicillin/streptomycin/l-glutamine were virtually identical (not shown).All experiments reported in this manuscript were conducted using the methods described above. Preliminary experiments were also performed after isolation of monocytes using the EasySep human monocyte enrichment kit with CD16 depletion (StemCell Technologies) and the EasySep human monocyte isolation kit (StemCell Technologies) with equivalent results. Analogous experiments were also performed with cells cultured in RPMI 1640 medium without supplementation of penicillin/streptomycin/2 for 15 min. The cells were further detached by pipetting and gently tapping the flasks. The suspension was aspirated into a new conical tube and another round of detachment with CellStripper was performed for 5 min. The detached cells were combined, centrifuged at 100 × g for 10 min, resuspended in RPMI medium, and combined with the non-attached cells. Cell counts were determined in duplicate using a Countess II automated hemocytometer (Thermo Fisher Scientific); at least two squares were counted for each replicate. All manipulations were performed in polystyrene conical tubes.For all assays, both non-attached and loosely attached moDCs were harvested and then combined. The culture supernatant containing the non-attached cells was first transferred to a conical tube. The remaining attached cells were then detached by addition of CellStripper , a non-enzymatic dissociation solution, to the flask  and incubation at 37°C and 5% COg for 10 min. The cells were resuspended in complete RPMI medium without cytokines. For readout by ELISA, cells were dispensed into flat-bottom 96-well plates in aliquots of 20,000 cells in 200 µL and incubated at 37°C and 5% CO2 for 2–4 hr. Each experiment contained medium-only (no cells) and PBS treatment (unstimulated/no treatment control) negative controls. For subsequent RNA isolation, cells were dispensed into flat-bottom 24-well plates in aliquots of 200,000–250,000 cells at 1 × 106 cells/mL, as indicated for each experiment, and incubated at 37°C and 5% CO2 for 2–4 hr. To initiate the stimulation, purified LPS or PBS (no treatment control) was added to each well to the final desired concentration. LPS stocks were generally prepared at a 20× concentration such that all wells received an equivalent volume of stimulant.To prepare moDCs for treatments, an aliquot of cells containing an appropriate cell number was centrifuged at 100 × 2 for 20 hr, at which point the supernatants were transferred into a V-bottom 96-well plate, centrifuged at 3200 × g for 10 min to remove residual bacteria and cell debris, transferred to new plates, and frozen at –30°C in aliquots.For readout by ELISA, the cells were incubated with the stimuli at 37°C and 5% CO2 for 2 hr. To harvest RNA from treated cells, a 3× volume of TRIzol LS reagent  or TRI Reagent  was added directly to the cells. The suspension was mixed by pipetting to lyse the cells, followed by RNA isolation using the Direct-zol RNA Miniprep kit (Zymo Research) including an on-column DNase I digestion step. Purified RNA was quantified using a Qubit Fluorometer (Thermo Fisher Scientific) and stored at –80°C until use.For RNA purifications, the cells were incubated with the stimuli at 37°C and 5% COTNF-α concentrations in undiluted or appropriately diluted supernatants were determined by ELISA using the SimpleStep human TNF alpha ELISA kit , performed following the manufacturer’s instructions and with endpoint absorbance measurements at 450 nm on an Infinite M200 Pro plate reader . For each experiment, absorbance measurements from wells containing a twofold dilution series of purified TNF-α  were used to calculate a calibration curve using a four-parameter logistic fit, which in turn was used to calculate TNF-α concentrations in all sample wells. Concentrations of CXCL10 and IL-10 were determined equivalently using SimpleStep human IP-10 ELISA kit (Abcam) and the SimpleStep human IL-10 ELISA kit (Abcam), respectively, following the manufacturer’s instructions. When handling multiple 96-well plates simultaneously, plates were staggered in 3-min intervals starting with the last wash step to ensure that incubation times with the development solution and stop solution were constant.To generate cDNA, purified RNA was reverse-transcribed using SuperScript III Reverse Transcriptase (Thermo Fisher Scientific) with oligo(dT) primers in the presence of RNaseOUT Recombinant Ribonuclease Inhibitor (Thermo Fisher Scientific) or using SuperScript IV VILO Master Mix (Thermo Fisher Scientific). All reactions in a given experiment were normalized to contain the same amount of RNA (250–600 ng depending on the experiment). cDNA was diluted 1:10 and stored at –30°C until use. qPCR was performed using the KAPA SYBR FAST qPCR Master Mix  in 20 µL reactions containing 3 µL diluted cDNA and 200 nM of each primer. Reactions were run on a LightCycler 480 Instrument (Roche). All reactions were performed in technical triplicates. RT-qPCR primers were chosen as intron-spanning primers, when possible, from the Universal ProbeLibrary (Roche), with the following sequences:ACTB: GCTACGAGCTGCCTGACG (fw), GGCTGGAAGAGTGCCTCA (rv)IFNB1: CTTTGCTATTTTCAGACAAGATTCA (fw), GCCAGGAGGTTCTCAACAAT (rv)TNF: CAGCCTCTTCTCCTTCCTGAT (fw), GCCAGAGGGCTGATTAGAGA (rv)CXCL10: GAAAGCAGTTAGCAAGGAAAGGT (fw), GACATATACTCCATGTAGGGAAGTGA (rv)RNA-seq libraries were prepared from purified RNA using the Stranded mRNA Prep Ligation kit  in 96-well format, following the manufacturer’s instructions. Input RNA amounts were held constant for all samples for a given donor, between 300 and 600 ng per reaction depending on the experiment. Final libraries were validated and quantified using the 2100 Bioanalyzer  using the High Sensitivity DNA kit (Agilent). Paired-end 100 or paired-end 150 sequencing was performed on a HiSeq 4000 (Illumina). Reads were aligned strand-specifically to the human genome (GRCh38) using the spliced read aligner STAR , versionAAVS1 was chosen from a previous report  with chemically synthesized sgRNAs (Synthego). Lyophilized sgRNAs targeting each gene  were resuspended to 100 µM  in RNase-free TE buffer  for 15 min at 25°C or overnight at 4°C with intermittent vortexing. Prior to use, sgRNA stocks were diluted to 25 µM in RNase-free H2O. Both stocks were stored at –30°C and freeze-thawed up to five times. To assemble RNP for electroporation of 4 × 105 cells, 50 pmol sgRNA and 20 pmol Cas9 were combined and diluted to 20 µL with nucleofection solution P1 or P3 . The mixture was incubated at 25°C for 10 min or up to 2 hr and immediately used to electroporate moDCs. For double knockouts, 50 pmol of sgRNA against each gene and 40 pmol Cas9 were combined in a total volume of 23 µL.RNPs were assembled by complexing purified recombinant Cas9 from 5 cells, 50 pmol crRNA:tracrRNA duplex and 20 pmol Cas9 v3  were combined and diluted to 5 µL in PBS, following the manufacturer’s instructions.For experiments with guide RNAs in the crRNA:tracrRNA format , lyophil5 cells per electroporation +5% excess) was transferred to a new conical tube and centrifuged at 90 × g for 10 min. The cells were resuspended in 1–5 mL PBS and centrifuged again at 90 × g for 10 min. For electroporation with RNPs assembled with sgRNAs , combined with 20 µL pre-formed RNP or nucleofector solution (no RNP control), and immediately electroporated using pulse code DJ-108 (solution P3) or other pulse codes, as described, using a Nucleofector-4D (Lonza) or a 96-well shuttle (Amaxa/Lonza) attached to a Nuclefector-4D. For electroporation with RNPs assembled with crRNA:tracrRNA duplexes 5 cells and 5 µL pre-formed RNP or nucleofector solution (no RNP control) was added. For no electroporation control cells, cells were treated identically, except that the corresponding cuvette well was not subjected to an electroporation pulse. Immediately after electroporation, 75 µL pre-warmed complete RPMI medium supplemented with 50 ng/mL GM-CSF and 20 ng/mL IL-4 were added to each well without disturbing the cells by letting the medium run down the side of the cuvette. After incubation at 37°C and 5% CO2 for 1 hr, the cells were mixed by pipetting and then split into two wells of a flat-bottom 96-well plate filled with 50 µL pre-warmed complete RPMI medium supplemented with 50 ng/mL GM-CSF and 20 ng/mL IL-4. The cells were incubated at 37°C and 5% CO2 for 5 days, with medium replenished after 2 or 3 days and then used for assays.Genome editing was performed by electroporation of moDCs with pre-formed Cas9 RNPs. moDCs were detached as described above. A suspension containing an appropriate number of moDCs (4 × 105 cells electroporated with 10 pmol Cas9 and 25 pmol sgRNA) was obtained through iterative grid searches of different conditions. In the process, several other conditions were also found to yield good results, including nucleofection solution P1 with pulse code CB-128  and used for phenotyping and genotyping.For all experiments in this manuscript, electroporated moDCs were harvested for phenotyping and genotyping 5 days post-electroporation, with the exception of data presented in 2 for 2–3 hr, and stimulated as described above. Each stimulation was performed in duplicate. Supernatants from stimulated cells were harvested and used to measure TNF-α levels as described above.To determine the responses of cells to stimuli by ELISA, aliquots of cells were transferred into flat bottom 96-well plates, diluted to 200 µL with complete RPMI medium without cytokines, incubated at 37°C and 5% CO5 to 2.5 × 105 cells were transferred into flat-bottom 24-well plates, diluted to 250 µL with complete RPMI medium without cytokines, incubated at 37°C and 5% CO2 for 2–3 hr, and stimulated as described above. Each stimulation was performed in duplicate. RNA was extracted from treated cells as described above.For subsequent RNA isolation, aliquots containing 1 × 10During the incubation prior to stimulation, aliquots of the remaining cell suspension were used to determine cell counts for each sample using a CellTiterGlo luminescence assay . Briefly, replicate aliquots of cells were transferred into an opaque flat-bottom 96-well plate, diluted to 100 µL, and incubated at 25°C for 15–30 min. After addition of an equal volume of CellTiterGlo solution to each well, the plates were placed on an orbital shaker for 2 min and then incubated at 25°C for 10 min. Finally, luminescence in each well was recorded using a GloMax Multi + luminescence plate reader (Promega). For some experiments, luminescence measurements from wells containing known numbers of unedited moDCs, as determined using a Countess II automated hemocytometer (Thermo Fisher Scientific), were used to calculate cell numbers for each sample. TNF-α secretion for each sample was then normalized to cell numbers. For other experiments, TNF-α secretion was simply normalized to background-subtracted luminescence readings. In benchmark experiments, cell counts were also determined by flow cytometry on an LSR-II flow cytometer (BD Biosciences) equipped with a 96-well autosampler. Cell counts determined by flow cytometry and luminescence were well correlated  and all To isolate genomic DNA from each sample for genotyping, aliquots of harvested moDCs were transferred to a 96-well V-bottom plate, centrifuged at 300 × g for 10 min, and resuspended in 50 µL QuickExtract DNA extraction solution . The suspensions were transferred to 96-well PCR plates and incubated at 65°C for 20 min and then at 98°C for 5 min using a thermocycler. The extracted genomic DNA was stored at –30°C until use.5 cells at the time of electroporation) were harvested as described above and combined. After setting aside cells for genomic DNA extraction, the remaining cells were used for antibody staining. In parallel, moDCs from the same donors that had not been subjected to the electroporation procedure and had instead been cultured in T-25 flasks were also harvested and stained. Stains were performed in 96-well V-bottom plates. Aliquots of 3 × 105 to 5 × 105 cells per well were first stained with the amine-reactive viability dye Ghost Dye Violet 510 (Tonbo) by washing the cells twice in PBS (without protein additives) followed by incubation with 0.1 µL dye in 100 µL PBS on ice for 30 min in the dark. The cells were then washed twice in PBS containing 10% FBS, resuspended in PBS containing 10% FBS, and stained in a total volume of 100 µL with an antibody mix containing the following antibodies at the indicated final concentrations: anti-CD11c , anti-HLA-DR , anti-CD83 , anti-B2M , anti-TLR4 . Fluorescence-minus-one controls for each of the five antibodies were included for unedited cells from each donor. Cells were incubated with the antibody mixes on ice for 30 min in the dark, washed three times in PBS containing 10% FBS, and finally resuspended in 200 µL PBS containing 10% FBS. To set compensation, an aliquot of 2 × 105 heat-killed moDCs (incubated at 65°C for 15 min) was stained with Ghost Dye Violet 510 in the same fashion and then mixed with 2 × 105 live moDCs and washed as described above, and 1:1 mixtures of CompBead anti-mouse Ig, κ beads (BD Biosciences) and CompBead negative control beads (BD Biosciences) were stained with each individual antibody at the same concentrations and washed as described above. Flow cytometry data were recorded on an Attune NxT flow cytometer (Thermo Fisher Scientific) and analyzed using FlowCytometryTools 0.5.0  were appended to the designed primer pairs. First-round PCRs of targeted sites were performed in 96-well format using at least 4000 genomic copies for each sample, 0.5 µM of each primer, and Q5 Hot Start High-Fidelity 2X master mix  and the following protocol: 98°C for 30 s; 35 cycles of 98°C for 10 s, 60°C for 30 s, and 72°C for 30 s; and a final extension at 72°C for 2 min. Products from the first PCR were diluted 1:100 in ddH2O and subjected to a second round of PCR using the constant adapters as annealing sites, appending Illumina P5 and P7 adapters and two eight-base barcodes on both ends that together uniquely identified each sample. Twelve cycles of PCR were performed using the same conditions described above. After the second PCR, all samples were pooled and the combined samples were purified using a 0.8× AMPure XP purification . Final libraries were validated and quantified using the 2100 Bioanalyzer (Agilent) using the High Sensitivity DNA kit (Agilent) and sequenced in a 600-cycle paired-end run on a MiSeq Instrument (Illumina) using MiSeq v3 Reagent kits.Genomic regions surrounding each cut site were PCR-amplified using a two-step protocol, largely as described . Brieflyhttps://github.com/jeffhussmann/knock-knock)  . For a fTo measure how amplicon size affects amplification and sequencing efficiency in our genotyping approach, we subjected pools of purified amplicons of defined sizes to our sequencing library preparation protocol and determined the resulting sequencing counts . BrieflyAmplicons are of defined sizes between ~150 bp and ~500 bp (the range of amplicon sizes in our experiments) and amplified by the same primer pair;Sequencing library preparation protocol is analogous to that used for genotyping, including similar effective template concentration and presence of excess non-productive genomic DNA;Amplicon abundance is measured before sequencing library preparation and compared to final sequencing counts to estimate amplification and sequencing efficiency.Briefly, we generated five amplicons with final lengths of 146 bp, 249 bp, 349 bp, 447 bp, and 539 bp and with constant annealing sites at the ends by PCR-amplifying different fragments of a gene encoding BFP with a constant forward primer and reverse primers positioned at the appropriate distances. Forward and reverse primers contained overhangs  to create annealing sites for sequencing library preparation. Following the PCR, each individual amplicon was gel-purified and quantified using a Qubit Fluorometer (Thermo Fisher Scientific). The five amplicons were then mixed into pools at 11 different molar ratios. For increased accuracy, the abundance of each fragment in these pools was measured using the 2100 Bioanalyzer (Agilent) using the High Sensitivity DNA kit (Agilent). Each pool was then diluted to 33 fM  and 1 µL of diluted pool was used as template for the first-round PCR as described above, using a primer pair complementary to the constant overhangs on each fragment, designed with the same criteria as our other amplicon primers. The PCRs additionally contained 4 µL of genomic DNA from unedited DCs as excess non-productive template. The remainder of the sequencing library preparation was carried out as described above, with unique sequencing indices appended to each pool in the second-round PCR. The final libraries were sequenced on a MiSeq (Illumina) and counts for each fragment were determined by aligning reads to the expected amplicons.im is the measured fractional abundance, is the starting abundance, and ie the fragment-specific observation efficiency. Note that the equation takes this form because we can measure only fractional rather than absolute abundances of each amplicon at the end; thusTo infer observation efficiency (amplification + sequencing) for each fragment, we reasoned that the starting and the final composition of the pool should be related by the specific observation efficiency of each amplicon, which should shift the abundance of each fragment based on its specific observation efficiency:ne = 1) and then solved the resulting linear equation systemie for each fragment in each pool. Because we expected per-cycle PCR amplification efficiency to be a major contributor to these efficiencies, we compared log2 e to fragment size and found it to be approximately linearly correlated  to 1 . For donor p, the RNA extraction for one replicate sample with knockout of  plastid . All othTo select genes to target in our arrayed genetic screen, we first included all genes from the following categories of pattern recognition receptors (PRRs): Toll-like receptors, NOD-like receptors, RIG-I-like receptors, C-type lectin receptors, Galectins, and SIGLECs. We then assembled a list of all genes encoding relevant signaling proteins downstream of these PRRs, including immediate adaptor proteins, kinases and ubiquitin ligases, the downstream transcription factors, as well as a limited subset of effector cytokines and cytokine receptors. Finally, we completed the gene list with additional genes of interest by surveying our RNA-seq data from human moDCs for expressed potential pattern recognition receptors such as predicted surface/membrane proteins, carbohydrate-binding proteins, and proteins containing a V-set domain using searches for Pfam domains, and by browsing the list of genes with the GO term ‘innate immune response’ that we had not yet included. This process ultimately resulted in a list of >400 genes. To narrow the list down to ~300 genes to enable screening in four 96-well plates, we first eliminated pseudogenes and a few PRR-like genes with well-established functions. We then eliminated many genes involved in linear signaling pathways while ensuring that each pathway was targeted at multiple nodes in the final library. In total, we targeted 291 unique genes of interest.CRX, KCNV1, TRHR, LALBA, RPE65, F13B, OR2D2, OR51T1, and TAS2R9) that are not expressed in moDCs, as assessed by our RNA-seq data and non-essential in any cell type surveyed at the time (from Project Achilles)  and POLR2A (a component of RNA polymerase II). In total, we included 14 controls. All sgRNA sequences are listed in The library additionally included four classes of controls: (1) non-targeting negative controls; (2) neutral controls (targeting negative controls); (3) targeting positive controls; and (4) essential controls. As non-targeting negative controls, we picked non-targeting negative controls #1 and #2 from Synthego. As neutral controls, we selected nine genes (chilles) , and foral genes : U2AF2  from a single healthy human donor and differentiated into moDCs as described above. Differentiated moDCs were electroporated with sgRNAs in 96-well format as described above. Plates were staggered in 10 min intervals to minimize the amount of time cells spent in nucleofection solution and the time delay between electroporation and addition of recovery media. On day 3 after electroporation, four wells of cells containing no pulse/no RNP control cells were harvested to assess responses of unedited cells to B. theta LPS and E. coli LPS and to determine an optimal B. theta LPS concentration for treatment of edited cells  were assigned the background-corrected absorbance of the sample with the lowest value greater than 0.Background-corrected absorbance values were normalized by the average luminescence (average of two replicate measurements) for each cell sample to calculate a cell count-normalized absorbance.For each sample, the cell count-normalized absorbance was normalized to the median cell count-normalized absorbance of all nine neutral targeting controls on the same 96-well plate to calculate a fold-change in TNF-α or IL-10 secretion. Normalization was performed by plate to normalize for any plate effects.2 transformed to calculate log2 fold-changes in TNF-α or IL-10 secretion.Fold-changes were log2 fold changes of replicate treatments were averaged to calculate the average log2 fold change of each knockout population. Normality tests suggested that the log2 fold-changes were generally normally distributed, rationalizing the averaging of the log2-transformed values.LogRaw and processed ELISA and cell count data are included in m of 60°C. Design criteria were successively relaxed if no primers matching these criteria were found, up to a maximum amplicon size of 500 base pairs and a minimum flanking distance of 25 base pairs. Primers containing overhangs as described above were ordered in 96-well format matching the sgRNA layout and tested for efficient amplification of the targeted locus by amplifying genomic DNA from unedited moDCs and sequencing the resulting amplicons on a MiSeq (Illumina), as described above. For loci with inefficient amplification or a high fraction of off-target amplicons, as assessed using knock-knock  were chosen. All primers are listed in To assess editing efficiency at all loci, amplification primer design and sequencing library preparation were streamlined to increase throughput. A first round of amplification primers was designed using PrimerServer , which uck-knock , primersck-knock  as descrSFTPA1 and SFTPA2), amplicons for both loci were detected with primer pairs designed to amplify each individual locus because it was impossible to design completely specific primer pairs with the criteria used. These amplicons were not excluded when calculating editing efficiency, such that editing efficiency is slightly underestimated for these loci. Results from outcome classification, after correction for size, are listed in Amplicon PCRs and sequencing sequencing library preparation were performed largely as described above, with the following modifications: (1) PCRs were performed in 384-well format; (2) first-round PCRs were set up using a Biomek FX liquid handling system with a 96-well head (Beckman Coulter); (3) first-round PCR products were diluted into Echo Qualified 384-Well Polypropylene Microplates using the Biomek FX; (4) PCR mastermix for the second-round PCR was dispensed into 384-well PCR plates using the Biomek FX; and (5) diluted first-round PCR products and indexing PCR primers were dispensed into the 384-well PCR plate using an Echo 525 acoustic liquid handler . Purification and validation of sequencing libraries, sequencing, classification of sequencing outcomes, and correction for amplicon size were performed as described above. For donor h, a small set of samples did not produce aligning sequencing reads in a first PCR attempt. These samples were repeated manually as described above, after which all but one sample produced aligning sequencing reads. Only successfully prepared samples were included for analysis. For donor i, the sequencing library preparation was repeated independently for >200 loci, which produced near-identical results , validatNo sample-size calculation was performed in advance. All results were reproduced in cells from multiple independent donors, following conventions of the field. Within independent experiments, assays were performed in duplicate or triplicate following conventions of the field.All main findings were derived from experiments with cells from at least two independent donors. The main hits from the genetic screen were validated in cells from two additional, independent donors. All treatments were performed in duplicate for readout by ELISA and qPCR and in duplicate or triplicate for readout by RNA-seq. Cell counts were generally conducted in duplicate. Information on number of replicates is contained in the figure legends.For identification of differentially expressed genes in RNA-seq, only genes with an average count >2 across all conditions were included for analysis. Exclusion criteria for editing analysis are described in the corresponding methods sections.https://github.com/jeffhussmann/knock-knock) (Amplicon sequencing data were processed using the publicly available pipeline knock-knock (k-knock) . RNA-seqk-knock) , featurek-knock) , and DESk-knock) . In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Jost and colleagues have developed a robust non-viral CRISPR/Cas9-based genome-editing methodology that enables to directly edit differentiated moDCs. This will open the possibility for functional genomics screening with DCs as well as targeted mechanistic studies that will enhance our biological understanding on the role of DCs for innate and adaptive immunity.Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Satyajit Rath as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Klio Maratou (Reviewer #1); Carla V Rothlin (Reviewer #2).Thank you for submitting your article "CRISPR-based functional genomics in human dendritic cells" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. As this manuscript comes under the category of "tools and resources article", it is important that the authors demonstrate that the editing is not changing the function of dendritic cells for the readers to have more confidence that the methodology is sound and will enable future studies on dendritic cells biology. Since the authors have already collected RNAseq data, they should capitalised on this dataset and conduct a more thorough analysis of this data. They need to compare the neutral control RPE65 KO vs. PBS samples to show that the editing does not significantly alter the transcriptomic profile of the dendritic cells. Can they see statistically significant differential expression changes between the two samples. Do these changes relate to pathways that are important for dendritic cell function? If so, what are these? Are any of the dendritic cell maturation or cell signalling pathways affected? Finally, it could be possible to perform immunophenotyping of the cells using fluorescent‐based flow cytometry with a panel of DC markers and other myeloid cell lineage markers to show that the process of editing does not de-differentiate/activate the cells or change their morphology.2. To validate and apply this strategy, the authors conducted a genetic screen for factors relevant to the response to LPS from different species as well as drivers of interindividual variation. The study is by and large is carefully designed and includes appropriate controls such as for example editing of the chemokine receptor CXCR4 which is not involved in the response to LPS and therefore serves as a negative control. However, weaknesses are noted in the application of the strategy to identify receptors for LPS or drivers of interindividual variability. The authors claim that their genetic screen allows them to identify receptors for LPS derived from B. theta as well as drivers of interindividual variation. Unfortunately, at this stage, the data does not fully support the authors conclusions. The authors should validate the potential role of TLR2 in an independent setting, for example making use of a TLR2 reporter assay. This is of particular importance for concluding that TLR2 is a receptor for LPS derived from B. theta.3. The response to B. theta varies in magnitude between donors even in non-edited cells. Is donor to donor variability too high to enable us to draw any biological conclusions when moDCs are used in screening? How many donors should be tested for biological phenotypic data to be meaningful?As noted by the authors the maximal response to LPS in terms of TNF production is different between the donors. Also, it appears that EC50s are different. In order to establish drivers of interindividual variation, the authors should compare the effect of knocking out gene X in cells from individual A and B when stimulated with EC50 concentrations of LPS.E. coli LPS than those from donor h ." The fact that unedited moDCs from donor i secrete less TNF-α in response to LPS is not sufficient support that PTPN6 and IL-10 constitutively suppress TNF-α secretion. The authors should test if moDCs spontaneously secrete TNF if knocked out for PTPN6 or IL-10?The authors emphasize differences in the effect of knocking out PTPN6 or IL-10 in the TNF secretion induced by LPS from B. theta. The authors go on to say (line 230) "Most prominently, knockouts of PTPN6 (SHP-1) and to a lesser extent IL10 increased TNF-α secretion in response to B. theta LPS in one donor ("donor i") but not the other ("donor h"), suggesting that these factors can constitutively suppress TNF-α secretion in a manner that differs among individuals . Indeed, unedited moDCs from donor i secreted less TNF-α in response to both B. theta LPS and In various instances, the authors should consider discussing the limitations of the approach and temper their conclusions: In this regard, the authors should revise their biological conclusions and stress that this only a proof-of-concept study as the screen was conducted with limited number of donors. The authors should be very careful with the interpretation on whether the genes that are hits from the screen are true drivers of interindividual variability. The authors clearly demonstrate that moDC responses to LPSs vary across individuals. This point highlights the importance of assessing immune phenotypes in donor-derived cells, but also raises the necessity to use multiple donors for meaningful and reliable conclusions to be drawn from the data. The number of biological donor replicates (2-3) used in this study is sufficient to show proof of concept, but subsequent studies using moDCs will require much larger number of donors. Essential revisions:1. As this manuscript comes under the category of "tools and resources article", it is important that the authors demonstrate that the editing is not changing the function of dendritic cells for the readers to have more confidence that the methodology is sound and will enable future studies on dendritic cells biology. Since the authors have already collected RNAseq data, they should capitalised on this dataset and conduct a more thorough analysis of this data. They need to compare the neutral control RPE65 KO vs. PBS samples to show that the editing does not significantly alter the transcriptomic profile of the dendritic cells. Can they see statistically significant differential expression changes between the two samples. Do these changes relate to pathways that are important for dendritic cell function? If so, what are these? Are any of the dendritic cell maturation or cell signalling pathways affected? Finally, it could be possible to perform immunophenotyping of the cells using fluorescent‐based flow cytometry with a panel of DC markers and other myeloid cell lineage markers to show that the process of editing does not de-differentiate/activate the cells or change their morphology.We agree with the reviewers that it is important to demonstrate that our method does not perturb DC function and will enable studies of dendritic cell biology. We now include additional data to support this point.First, we characterized edited and unedited DCs from two independent donors by flow cytometry, measuring levels of CD11c (a DC marker), HLA-DR and CD83 (two markers whose expression increases during DC maturation), TLR4 (expression of which decreases during DC maturation), and B2M . We did not include additional markers because we already occupied 6 colors (including a viability stain) and we wanted to balance the ability to quantify marker levels against the breadth of the panel. As shown in Figure 1—figure supplement 5A, the staining patterns are qualitatively indistinguishable in knockout DCs and in unedited DCs (no electroporation control DCs or DCs electroporated with a non-targeting control sgRNA). We observed small differences between expression profiles of edited DCs and unedited DCs that were grown in flasks, pointing to changes in DC state as a consequence of culture conditions; such changes can be controlled by comparing knockout DCs to identically cultured unedited DCs or to DCs with knockout of a neutral gene, as we did throughout the manuscript. Overall, these results suggest that moDCs do not undergo de-differentiation or maturation as a consequence of the editing procedure.We also stained for B2M, but we designed the panel for maximal resolution in the other channels, precluding accurate quantification of reductions in B2M levels upon knockout. We can place a conservative lower bound of >70% depletion for B2M. More broadly, our functional assays clearly demonstrate a reduction in protein levels in knockout cells.The flow cytometry data are presented in Figure 1—figure supplement 5 in the revised manuscript and described in a new paragraph in the Results section. We also included details of the experimental strategy in the methods section.To compare the transcriptional states of edited and unedited DCs, we compared RNA-seq data from mock-treated edited cells  and from mock-treated unedited cells . Clustering of the transcriptional profiles revealed that transcriptional states of edited DCs can be more similar to those of unedited DCs than those of unedited DCs from different donors to each other, suggesting that any changes induced by the editing procedure are smaller than any natural variation in DC state. In addition, the expression levels of a panel of DC markers are comparable between edited and unedited DCs. These analyses provide further evidence that the editing procedure does not substantially alter DC state. These data are included in the revised manuscript as Figure 2—figure supplement 3 and are described in the Results section.We realize that this analysis may not be the one the reviewers had in mind. We could not perform the suggested direct comparison between unedited and RPE65 KO DCs because we did not include unedited DCs from the same donors in the RNA-seq experiment on knockout DCs. Indeed, throughout the manuscript we used DC populations with knockout of a neutral gene  as the control populations to which we compared all other data. In our view this is the most robust way to ensure that the comparisons isolate the effect of the knockout.Together with our data demonstrating that edited moDCs retain the ability to respond to diverse innate immune ligands , the flow cytometry data and the additional analyses of the RNA-seq data provide strong support that our editing procedure does not perturb DC state and function.2. To validate and apply this strategy, the authors conducted a genetic screen for factors relevant to the response to LPS from different species as well as drivers of interindividual variation. The study is by and large is carefully designed and includes appropriate controls such as for example editing of the chemokine receptor CXCR4 which is not involved in the response to LPS and therefore serves as a negative control. However, weaknesses are noted in the application of the strategy to identify receptors for LPS or drivers of interindividual variability. The authors claim that their genetic screen allows them to identify receptors for LPS derived from B. theta as well as drivers of interindividual variation. Unfortunately, at this stage, the data does not fully support the authors conclusions. The authors should validate the potential role of TLR2 in an independent setting, for example making use of a TLR2 reporter assay. This is of particular importance for concluding that TLR2 is a receptor for LPS derived from B. theta.B. theta LPS recognition and of PTPN6 in mediating inter-individual variation, were intriguing but not essential to establish the utility. We therefore re-framed the manuscript to more clearly emphasize the methods development and proof-of-principle nature of the screen, while presenting the additional points as hypotheses for further exploration.We agree with the reviewers that some of our claims needed further support to draw definitive conclusions. However, as the reviewers pointed out in the first and last main comments, the main innovation in our manuscript is the method to introduce knockouts in DCs. The genetic screen recapitulated known LPS signaling pathways and thus provided proof-of-principle for the utility and scalability of our method. The additional hypotheses emerging from the screen data, such as the potential roles of TLR2 in 3. The response to B. theta varies in magnitude between donors even in non-edited cells. Is donor to donor variability too high to enable us to draw any biological conclusions when moDCs are used in screening? How many donors should be tested for biological phenotypic data to be meaningful?B. theta WT LPS is a weaker agonist than E. coli LPS and B. theta 4PP LPS has further reduced activity in all donors. In addition, our genetic screen identifies many core factors that are important for LPS recognition across donors. Thus, many biological conclusions can be drawn from moDCs by interrogating cells from 2-4 different donors, as we did throughout the manuscript.First, although we observe that the magnitudes of the LPS response varies across DCs from different donors, many other core aspects of biology are consistent across donors. For example, the patterns of activity across different LPSs are consistent: Second and perhaps more broadly, inter-individual variability is an interesting biological phenomenon in itself, but how it arises from environmental and genetic factors has long been difficult to study. In this context, the ability to conduct genetics in moDCs from different individuals will be essential to pick apart these contributions, for example through longitudinally sampling cells from the same donor to disentangle genetic and environmental contributions. We provide first glimpses of how we can begin understanding the drivers of such variation by comparing cells from 2-4 different donors. We agree that to definitively pinpoint drivers, studies in larger numbers of donors will be needed, as further discussed below. Importantly, we observed high knockout efficiency across all donors , suggesting that the utility of our method is not limited by inter-individual variation and will be applicable to studies involving large numbers of donors.We touch upon these points in the Discussion section of the revised manuscript.As noted by the authors the maximal response to LPS in terms of TNF production is different between the donors. Also, it appears that EC50s are different. In order to establish drivers of interindividual variation, the authors should compare the effect of knocking out gene X in cells from individual A and B when stimulated with EC50 concentrations of LPS.We used non-saturating concentrations of LPS for all experiments with knockouts. We do not think we would be able to learn more about the drivers of inter-individual variation by stimulating at exactly EC50.E. coli LPS than those from donor h ." The fact that unedited moDCs from donor i secrete less TNF-α in response to LPS is not sufficient support that PTPN6 and IL-10 constitutively suppress TNF-α secretion. The authors should test if moDCs spontaneously secrete TNF if knocked out for PTPN6 or IL-10?The authors emphasize differences in the effect of knocking out PTPN6 or IL-10 in the TNF secretion induced by LPS from B. theta. The authors go on to say (line 230) "Most prominently, knockouts of PTPN6 (SHP-1) and to a lesser extent IL10 increased TNF-α secretion in response to B. theta LPS in one donor ("donor i") but not the other ("donor h"), suggesting that these factors can constitutively suppress TNF-α secretion in a manner that differs among individuals . Indeed, unedited moDCs from donor i secreted less TNF-α in response to both B. theta LPS and PTPN6 or IL10 increases TNF- α secretion upon stimulation. We re-phrased the corresponding section to clarify. More broadly, we now more clearly emphasize that this hypothesis requires further exploration.We did not clearly state our model. Our hypothesis is that PTPN6 and IL-10 suppress TNF-α secretion upon stimulation of the cells, such that knockout of In various instances, the authors should consider discussing the limitations of the approach and temper their conclusions: In this regard, the authors should revise their biological conclusions and stress that this only a proof-of-concept study as the screen was conducted with limited number of donors. The authors should be very careful with the interpretation on whether the genes that are hits from the screen are true drivers of interindividual variability. The authors clearly demonstrate that moDC responses to LPSs vary across individuals. This point highlights the importance of assessing immune phenotypes in donor-derived cells, but also raises the necessity to use multiple donors for meaningful and reliable conclusions to be drawn from the data. The number of biological donor replicates (2-3) used in this study is sufficient to show proof of concept, but subsequent studies using moDCs will require much larger number of donors.We agree with the reviewers that a discussion of the strengths and limitations of the approach was missing from the manuscript and have included such a discussion in the revised manuscript at the beginning of the Discussion section.We also agree that studies in larger numbers of donors will be needed to definitively establish the roles of putative drivers of inter-individual variation, which we also note in the revised Discussion. More broadly, we now emphasize that our study provides proof-of-principle, suggests exciting hypotheses to be explored further, and provides enabling tools for such explorations.


Sleep disorders have been increasingly investigated in several medical illnesses as their presence may affect patients’ quality of life. However, the research examining sleep disorders in oral cancer is relatively weak. Indeed, the majority of the available studies present a cross-sectional or retrospective designs. Moreover, very few of them have evaluated quality of sleep in oral cancer survivors (OC survivors). We aimed to carry out a case-control study with the purpose to investigate sleep disorders and mood impairment in 50 OC survivors. Our research has shown that quality of sleep is significantly affected in OC survivors compared to a healthy population and that OC survivors suffers from higher levels of anxiety and depression. Our results may suggest that an appropriate assessment of quality of sleep and psychological profile should be performed in OC survivors as a prompt treatment for both sleep and mood disorders is crucial for the overall improvement of patients’ quality of life.p-value: <0.001). QoS of OC survivors was significantly impaired, especially with regard to some PSQI sub-items as the subjective sleep quality, sleep latency and daytime dysfunction . Moreover, poor QoS was negatively correlated with years of education  and positively correlated with alcohol consumption  and with the use of systemic medications . Sleep disorders and mood disorders are common comorbidities in OC survivors; therefore, early assessment and management before, during and after treatment should be performed in order to improve the quality of life of OC survivors.Quality of sleep (QoS) and mood may impair oral cancer survivors’ wellbeing, however few evidences are currently available. Therefore, we aimed to assess the prevalence of sleep disorders, anxiety and depression among five-year oral cancer survivors (OC survivors). 50 OC survivors were compared with 50 healthy subjects matched for age and sex. The Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), the Hamilton Rating Scales for Depression and Anxiety , the Numeric Rating Scale (NRS), the Total Pain Rating Index (T-PRI) were administered. The global score of the PSQI, ESS, HAM-A, HAM-D, NRS, T-PRI, was statistically higher in the OC survivors than the controls ( Oral cancer is a life-threatening disease and a burden for health care systems worldwide. According to Global Cancer Statistics, GLOBOCAN, there were 354,864 new cases of oral cavity cancer causing 177,384 deaths during 2018 ..12].The recruitment of OC survivors and healthy subjects was conducted between January and September 2018 and was based upon convenience sampling. All potentially eligible individuals were invited to participate in the present study and provided their written informed consent.The case and the control groups were matched by age and gender. Specifically, first we recruited the patients and then calculated the gender distribution and the average age; secondly, we recruited the controls to obtain a matched sample.Participants of either gender and aged 18 or older were included. The inclusion criteria for the OC survivors’ group were: (i) clinical and histopathological findings of oral squamous cell carcinoma (OSCC) or tobacco-related verrucous cell carcinoma (VCC) (ii) patients with a follow-up of at least five years after the diagnosis of OSCC or VCC and being free from malignancy for at least one year, (iii) all stages based on the American Joint Committee on Cancer Staging Manual 8th edition and (iv) patients managed by surgery, radiotherapy and/or chemotherapy.On the contrary, the exclusion criteria for the case group were: (i) patients affected by human papillomavirus (HPV)-related OSCC, (ii) patients affected by another type of tumor localized at the head and neck region, (iii) patients who had concomitant tumors in another organ, and (iv) patients who had experienced severe and irreversible side effects from OSCC treatment such as fibrosis, a mouth opening restriction of less than 30 mm, trismus, hyposalivation or osteoradionecrosis of the jaw.The inclusion criteria for the control group were: (i) patients treated at the University Dental Clinic only for routine dental care during the study period; and (ii) the absence of any oral mucosal lesions or any previous history of OSCC/VCC.For both groups the exclusion criteria were (i) breastfeeding or pregnant participants, (ii) patients affected by autoimmune disease or another debilitating condition or unstable disease (such as osteonecrosis of the jaw or dementia), (iii) participants with a medical history of a psychiatric disorder as defined by the DSM-5 or regularly treated with a psychotropic drug, (iv) drug-addicted or alcoholic participants and (v) individuals unable or not willing to give their consent or to understand and complete the questionnaires.A comprehensive intra- and extra-oral examination was carried out by two oral medicine experts (RG and AD). Upon admission, demographic data such as gender, age, educational level (in years), marital status, employment status, risk factors  body mass index (BMI), comorbidities and associated drug use were recorded for both groups.Details of clinical oral cancer related characteristics were also noted for the case-group, such as the clinical stage and grading at the time of diagnosis, the location of the tumor, any clinical nodal involvement, any metastasis, the type of treatment, and any need for further treatment during the 5-year follow-up. The performance status was assessed using the Eastern Cooperative Oncology Group (ECOG) scale in OC survivors whose scores range from 0 (fully active) to 5 (death), with higher values indicating a poorer performance status .-the Pittsburgh Sleep Quality Index (PSQI)  for the -the Epworth Sleepiness Scale (ESS)  for the -the Hamilton Rating Scale for Depression (HAM-D)  and the -the Numeric Rating Scale (NRS)  and the A predefined set of questionnaires was given to the participants of both groups in order to assess their quality of sleep (QoS), their psychological status (level of anxiety and depression) and the intensity and quality of any pain. The questionnaires comprised:The Pittsburgh Sleep Quality Index (PSQI) is a standardized questionnaire used for the assessment of the QoS and the incidence of sleep disturbances. This tool consists of 19 items which generate 7 ‘component’ scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication and daytime dysfunction. The scores for each item range from 0 to 3, with higher scores indicating a poorer QoS. The items are combined to yield the seven components, each component having a score ranging from 0 to 3, and the sum of the scores for these seven components yields a global score ranging from 0 to 21. Global scores above five distinguish poor sleepers from good sleepers with a high sensitivity (90–99%) and specificity (84–87%) .The Epworth Sleepiness Scale (ESS) is used to measure an individual’s general level of daytime sleepiness. The tool consists of 8 items assessing the propensity for sleep in eight common situations. Subjects rate their likelihood of dozing in each situation on a scale of 0 (would never doze) to 3 (would have a high chance of dozing). The ESS score is the sum of the eight items, ranging from 0 to 24, with a cut-off value of >10 indicating excessive daytime sleepiness .The Hamilton Rating Scale for Anxiety (HAM-A) is a measure of symptoms of anxiety and it consists of 14 items. Scores can range from 0 to 56, with scores from 7 to 17 indicating mild symptoms, between 18 and 24 indicating mild-to-moderate severity, and >25 indicating moderate-to-severe anxiety .The Hamilton Rating Scale for Depression (HAM-D) is a measure of symptoms of depression that is comprised of 21 items pertaining to the affective field. Scores can range from 0 to 54. Scores between 7 and 17 indicate mild depression, between 18 and 24 moderate depression, and over 24 severe depression [The Numeric Rating Scale (NRS-11) is a well-validated instrument for the evaluation of pain intensity. whose scale ranges from 0 to 10 . Respondents are asked to report pain intensity in the last 24 h .The Total Pain Rating Index (T-PRI) from the short form of the McGill Pain Questionnaire (SF-MPQ) is a measure of the quality of pain and it is a multidimensional pain questionnaire which measures the sensory, affective and evaluative aspects of the perceived pain. It comprises 15 items from the original MPQ, each scored from 0 (none) to 3 (severe). The T-PRI score is obtained by summing the item scores (range 0–45). There are no established critical cut-off points for the interpretation of the scores and, as for the MPQ, a higher score indicates worse pain .t-test procedure. The significance difference between the recorded medians of the PSQI, ESS, HAM-D, HAM-A, NRS and T-PRI, was measured by the Mann-Whitney Test.Descriptive statistics, including means, standard deviations, medians and the inter-quartile range (IQR) were used to analyse all the socio-demographic and clinical characteristics of the two groups. For the qualitative variables, the significance was calculated by the Exact Chi Square Test. For the demographic numerical variables the significance difference between means was calculated by the parametric two-samples p-value < 0.05 (two-tails) was considered as statistically significant.The addition of the clinical characteristics predictors of a poor QoS in OC survivors, hierarchical multiple regression analyses were performed and unadjusted coefficient estimations were obtained for each predictor. A total of six models was computed. The coefficient estimated for binary variables, such as smoking and alcohol consumption, measures the effect of the Yes response on the outcome estimation. For each model, we reported the adjusted R2 which measures the overall goodness of fit adjusted for the number of variables included into the model. The demographic model (model 1) was performed to test the contribution of the demographic variables to a poor QoS. Next, the clinical model (model 2), the psychological model (model 3), the daytime sleepiness model (model 4) and the pain model (model 5) were each performed after controlling for demographic variables to test the contribution of the clinical variables of the OSCC, anxiety and depression , daytime sleepiness (ESS), intensity and quality of pain  to a poor QoS. Finally, a standard regression analysis (model 6) was computed by entering all the variables simultaneously into the model in order to determine the relative contributions of all the variables to a poor QoS. In all the steps, standard errors of the model coefficients, which measure the statistical precision of the inference estimation of the model parameters, were provided. The IBM SPSS version 22.0 was used to conduct all the statistical analyses in this study, and The demographic characteristics, BMI and habits of the case and control groups are summarized in n = 26) and 46% (n = 24) were male and female for each group, respectively, with a mean age of 59.5 ± 10.1 years for the cases and 65.1 ± 14.4 years for the controls . No statistically significant difference was found in terms of marital status, years of education, BMI or alcohol consumption . However, the number of healthy participants in full-time employment and with a current smoking habit was significantly higher  in comparison to the case group.Of these participants, 54% , especially with respect to hypertension, hypercholesterolemia, prostatic hypertrophy and gastrointestinal diseases . Consequently, the number of OC survivors taking medications, such as angiotensin II receptor antagonists, beta blockers, proton pump inhibitors and statin agents was significantly higher compared to the controls .At the time of the assessment, 66% of the OSCC patients presented with an ECOG performance status of 0 (“fully active”) and 34% with an ECOG performance status of 1 .Among the OC survivors, 52% were poor sleepers (PSQI > 5), whereas only 12% of the controls reported a poor QoS. Moreover, mild to severe anxiety was reported in 84% of the OC survivors  along with mild to severe depression in 74% of cases . On the contrary, only 20% and 18% of the healthy participants showed mild anxiety and depression symptoms, respectively, and no cases of moderate to severe anxiety or depression were recorded in the control group.p-value: 0.017 *), especially for the items “subjective sleep quality”, “sleep latency” and daytime dysfunction” , and in the total ESS score  in comparison with the controls. Furthermore, statistically significant higher levels of anxiety and depression, as reflected by the total scores of the HAM-A and HAM-D, were also recorded among the OC survivors , together with higher levels of oral discomfort and pain according to the NRS and T-PRI total scores . Taken together, these findings suggest that QoS and psychological status may be severely impaired in OC survivors.p-values: <0.001 **, <0.001 ** and 0.019 * respectively) but not with the ESS and the NRS. Specifically, the majority of the PSQI sub-items (except for “use of sleep medication” and “sleep latency”) were positively correlated with the HAM-A and HAM-D (except for “use of sleep medication”), whereas the T-PRI was correlated only with “sleep disturbances and daytime dysfunction” which also correlated, as expected, with the ESS. Overall, patients with a poorer QoS presented with higher levels of anxiety and depression and a worse quality of pain but not with increasing daytime sleepiness or pain intensity  and resulted in a strongly significant increase in the coefficient of determination (R2) . The addition of the clinical characteristics showed that the PSQI was positively correlated with alcohol consumption  and with the use of systemic medications . When entering all the variables simultaneously in the second model, we found an increase in the R2 value with a ∆R2 of 6.2%, possibly due to both the parameters, namely alcohol consumption and medications, although it was not statistically significant . The third model , testing the contribution of anxiety and depression to QoS, showed that the PSQI was positively correlated with the HAM-A and HAM-D  and resulted in a strongly significant increase in the R2 . The daytime sleepiness and pain models (models 4 and 5) did not result in a significant increase in the R2 value . The final full model  in which all of the variables were entered simultaneously  resulted in a moderate increase in the R2 value  and could explain the 44.3% of variance of poor QoS. In this last model, depression has shown a strong correlation to sleep disorders  contributing significantly to a poor QoS.The hierarchical multiple regression analyses predicting QoS are shown in The aim of this study has been to investigate the prevalence of sleep disorders (insomnia and hypersomnolence), anxiety and depression in OC survivors with a 5-year follow-up and to analyze potential predictors in the development of sleep disorders. The detection and treatment of factors which could influence the well-being of OC survivors are becoming increasingly important for healthcare systems in order to improve the follow-up care of these patients.Among this population, insomnia, poor QoS, short sleep duration, excessive daytime sleepiness and sleep-related breathing are commonly reported and tend to become often chronic and pervasive in patients during and after treatment for OSCC .In a recent systematic review, the prevalence of self-reported insomnia (defined with a PSQI cut-off of 5) in patients with head and neck cancer was 29% before treatment, 45% during treatment and 40% after treatment, while the prevalence rate of hypersomnolence (ESS cut-off > 10) was 16% before and 32% after treatment .In this study, a higher prevalence of insomnia among the OC survivors within the 5-year follow-up was found, in comparison with the study of Santoso et al.  as 52% oWith regard to the PSQI components a higher percentage of OC survivors reported an impaired subjective sleep quality, sleep latency, and daytime dysfunction.Pain, fatigue, medical treatment, psychological profile (anxiety and depression) and comorbidities  may causIn a previous study, a lower education level, the presence of systemic comorbidities and the use of systemic drugs, adversely affected quality of life outcomes in survivors of cancer . MoreoveOur results are in line with these studies, suggesting that the use of medications for systemic comorbidities could have a detrimental effect on the life of patients that over time could also influence QoS. However, medications with alcohol consumption contributed to sleep disorders on the account of 6.2% of the variance of poor QoS based on the second model of the regression analysis which suggests that medications may not have a pivotal role in explaining the higher prevalence of sleep disorders in this group of OC survivors, possibly for the absence of sleep disorder breathing and obstructive sleep apnea in our sample.In addition, the low intensity of pain (NRS: 2) reported by OC survivors is considered as a predictor of poor sleep, as suggested by the regression analysis. Although xerostomia was not detected in our sample of patients probably because radiotherapy was prescribed in only 16% (8) of patients, Shuman et al  similarlRegarding habits, alcohol abuse and tobacco smoking might play a role in the development of sleep disorders. Indeed, heavy alcohol users often experience insomnia even after they stop their alcohol consumption, while smokers suffer more frequently from poor sleep, compared with non-smokers ,28. In tWhile in a recent study insomnia and hypersomnolence were found to be associated with chemotherapy and radiotherapy,  in the pPrevious studies have suggested that obesity BMI > 30) is considered a significant predictor of sleep disorders  is consi.Regarding the psychological profile, the current literature has reported a prevalence of anxiety and depression, ranging from 19 to 50%, in cancer survivors, suggesting that the burden of cancer diagnosis and its treatment could have a strong impact on the psychological profile, persisting over time despite a successful operation and subsequently decreasing the quality of life of the affected patients. Moreover, Espie et al. reported that from 22% to 32% of OC survivors were anxious or depressed even ten years after the diagnosis and treatment . As a coIn this study, a higher prevalence of mood disorders has been found in comparison with the current literature; indeed, anxiety and depression were identified in 84% (42%) and 74% 37) of OC survivors, respectively. In addition, in the final full model, depression was found to be the most contributive factor to poor QoS. The higher level of depression may be related to the stress associated with a fear of cancer recurrence, since almost 40% [7 of OC sMood disorders and poor sleep were closely interconnected, as shown by the correlation analysis. In addition, anxiety and depression were predictors of poor sleep, as confirmed by the regression analysis. No differences between male and female patients were detected, and neither the stage and treatment nor the number of operations for cancer recurrence affected the incidence of sleep disorders. In line with previous studies, an impaired mood and sleep affected the functional recovery of patients and their return to work because, despite their age, the majority of OC survivors (48%) had retired.The results of this study suggest that the high prevalence of insomnia may be related not only to psychiatric symptoms or to a fear of cancer recurrence but could also be considered in some cases an independent variable  which needs to be addressed regardless of all the other factors. It is possible to consider that cancer itself can lead to the development of sleep disorders through inflammation. Inflammation has emerged as a crucial pathway which may be especially relevant with respect to cancer survivors. The sleep-wake cycle has emerged as a homeostatic regulator of inflammatory biology in which sleep loss induces an activation of nuclear factor KB (NF-kb)  and circAdequate sleep is a biological requirement for healthy physical, cognitive and psychological functioning so the management of sleep disturbance should be targeted by clinicians with appropriate interventions. In particular, the prominent role of cognitive behavior therapy has been studied . * AdditThe findings of the current study should be understood in the light of some limitations. First, the sample is small and all the patients were recruited at a single hospital, thus preventing the possibility of any geographical generalizability and slightly affecting the power of the regression analyses. Secondly, the exclusion of patients who had developed severe and permanent side effects due to the radiotherapy, may have produced a potential underestimation of the prevalence of sleep disorders in OC survivors. Moreover, the study design does not allow the drawing of any conclusive inferences about the temporality and causality of the relationships between the variables explored. Finally, only subjective sleep quality was investigated in this study, with objective sleep quality not being considered, and therefore additional measurement systems should be incorporated to verify our findings.Sleep disorders (including insomnia and hypersomnolence) continue to be prevalent both during and after treatment for OSCC. A lower level of education, the use of systemic drugs, the consumption of alcohol and the presence of anxiety and especially depression are predictors of poor sleep in OC survivors.The treatment of oral cancer must clearly remain the major goal, but the treatment of any psychological comorbidities is also important in order to improve the quality of life in these patients. Therefore, healthcare professionals should be encouraged to include sleep disorders assessment at the time of diagnosis, during treatment and in follow-up consultations. Further clinical and prospective studies should be conducted not only to evaluate the real prevalence of sleep disorders but also to plan an adequate treatment over time with respect to all OC survivors.