Zinc-finger protein 768 (ZNF768) is a transcription factor that was recently shown to promote proliferation and repress senescence downstream of growth factor signaling in vitro. This protein was found to be overexpressed in a small cohort of lung adenocarcinoma (LUAD) compared to normal tissue, but its clinical value in this cancer remains unknown. The aim of this study was to determine whether ZNF768 associates with clinicopathological features in LUAD. We found that ZNF768 levels are often elevated in LUAD and that ZNF768 protein levels positively correlate with Ki-67 and other proliferative clinicopathological features in this cancer. Supporting a role for ZNF768 in promoting proliferation, ZNF768 depletion severely impairs proliferation in several lung cancer cell lines. Our study, that extends previous in vitro data, provides the first clinical observations supporting a possible role for ZNF768 in promoting cancer cell proliferation and tumor development in humans.Lung adenocarcinoma (LUAD) is the most common type of lung cancer and a leading cause of cancer-related deaths worldwide. Despite important recent advances, the prognosis for LUAD patients is still unfavourable, with a 5 year-survival rate close to 15%. Improving the characterization of lung tumors is important to develop alternative options for the diagnosis and the treatment of this disease. Zinc-finger protein 768 (ZNF768) is a transcription factor that was recently shown to promote proliferation and repress senescence downstream of growth factor signaling. Although ZNF768 protein levels were found to be elevated in LUAD compared to normal lung tissue, it is currently unknown whether ZNF768 expression associates with clinicopathological features in LUAD. Here, using tissue microarrays of clinical LUAD surgical specimens collected from 364 patients, we observed that high levels of ZNF768 is a common characteristic of LUAD. We show that ZNF768 protein levels correlate with high proliferative features in LUAD, including the mitotic score and Ki-67 expression. Supporting a role for ZNF768 in promoting proliferation, we report that ZNF768 depletion severely impairs proliferation in several lung cancer cell lines in vitro. A marked decrease in the expression of key proliferative genes was observed in cancer cell lines depleted from ZNF768. Altogether, our findings support a role for ZNF768 in promoting proliferation of LUAD. Lung cancer is amongst the most common cancer worldwide and is the leading cause of cancer-related deaths, accounting for 1.76 million deaths annually in men and women ,3,4. RecEGFR, K-RAS, MET) and phosphoinositide-3-kinase (PI3K)/mechanistic target of rapamycin (mTOR)  pathways, inactivation of the p53 pathway  and repression of key cell cycle regulators .Lung cancer is classified into two main histological groups based on their clinical behavior, small cell lung carcinoma (SCLC) and non-SCLC (NSCLC), with NSCLC representing the majority of the cases. This group can further be divided into adenocarcinoma, squamous cell carcinoma and large cell carcinoma. Lung adenocarcinoma (LUAD) is the most common type of NSCLC and constitutes almost half of all lung cancer cases ,9. This ZNF768 overexpression is common in a variety of human cancers suggesting that elevated levels of ZNF768 could serve as a way to bypass oncogene-induced senescence and sustain tumor proliferation. We confirmed this finding in a small cohort of NSCLC where we observed elevated protein levels of ZNF768 in many tumors compared to matched lung tissue [Zinc-finger protein 768 (ZNF768) is a transcription factor that has been shown to greatly impact the proliferation and the survival of cancer cells in vitro ,14. We rg tissue . AltogetDespite evidence showing that ZNF768 expression is elevated in lung tumors, it is currently unknown whether these findings extend to a larger cohort and whether ZNF768 expression associates with clinicopathological features in LUAD. Here, using tissue microarrays, we report that high levels of ZNF768 is a common feature in these tumors. Furthermore, ZNF768 expression measured with the immunohistochemistry score (H-score) correlates with high proliferative clinicopathological features including the mitotic score and Ki-67 expression in the tumors. Further supporting the proliferative signature associated with ZNF768 in LUAD, knockdown of ZNF768 in different lung cancer cell lines severely impaired proliferation and decreased the expression of key cell cycle effectors. Altogether, our findings show that ZNF768 protein levels are elevated in LUAD, that ZNF768 associates with proliferative markers in this cancer and that this transcription factor is required to support cancer cell proliferation. Our study, that extends previous in vitro data, provides the first clinical observations supporting a possible role for ZNF768 in promoting cancer cell proliferation and tumor development in humans.The study population included a selection of 577 patients that underwent a lung surgical resection for an adenocarcinoma between 2003 and 2012 at the Institut Universitaire de Cardiologie et de Pneumologie de Québec (IUCPQ). A consent form was obtained by the institution’s Biobank from all participants. Clinical and pathological data, including age, sex, smoking status, tumor stage (American Joint Committee on Cancer AJCC 8th edition) and overall survival, were retrieved using the electronic medical files and pathology reports. A thoracic pathologist (PJ) reviewed each tumor’s histologic slides and a representative area of the lesion was identified on one of them. The corresponding formalin-fixed, paraffin-embedded tissue blocks were retrieved, and tissue microarrays (TMA) were prepared by punching the donor block in the corresponding identified area on hematoxylin and eosin (H&E) slide. Tumor representative cores of 1.0 mm in diameter were placed 1.0 mm apart from each other on the recipient TMA paraffin block with a tissue arrayer .Five-micrometre-thick sections were cut from the TMA blocks on a microtome and placed on charged slides. For ZNF768 immunohistochemistry (IHC)  slides underwent heat-induced epitope retrieval in a DAKO PT-Link using EnVision FLEX Target Retrieval Solution, Low pH (Citrate buffer pH 6.1), followed by an automatized IHC protocol on Dako Autostainer Link 48 . A previous validation set with positive tissues allowed to set ZNF768 antibody dilution to 1:100 with an incubation time of 20 min. The remaining of the IHC procedure was a standard Autostainer protocol using Dako EnVision FLEX+ kit reagents. After IHC, slides were counter-stained using EnVision FLEX hematoxylin. Ki-67 and p53 IHCs  were carried out on the same platform according to the manufacturer’s specific protocols.Scoring of ZNF768, p53 and Ki-67 were done on digitalized slides. Digital images of each TMA stained with standard H&E and all IHCs at 20 × magnification were obtained using a slide scanner . Whole slide images were visualized using the companion software . Cores were excluded if they had fewer than a hundred tumor cells. ZNF768 and p53 expression were evaluated using the H-score method. This method combines the percentage of stained nuclei and the staining intensity using the following equation to obtain a score between 0 and 300: % nuclei of intensity 1 × 1 + % nuclei of intensity 2 × 2 + % nuclei of intensity 3 × 3. Ki-67 expression was evaluated using the percentage of stained nuclei on a scale of 0 to 100%.H-score and background as well as clinical indicators were investigated using parametric  or non-parametric (Spearman rho) analyses, depending on the nature of the variables and their distribution. All analyses were two-tailed with α = 0.05.Associations with the ZNF768 All cell lines  were obtained from American Type Culture Collection . A549 and 293T cells were cultured in complete Dulbecco’s Modified Eagle Medium (DMEM), while NCI-H441 and NCI-H460 cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium. Both mediums were supplemented with Fetal Bovine Serum (FBS) (10%)  and penicillin-streptomycin (1%) .Lentiviral shRNAs were obtained from the collection of The RNAi Consortium (TRC) at the Broad Institute . These shRNAs are named with the numbers found at the TRC public website: sh_Luciferase (TRCN0000072246), shZNF768_1 (TRCN0000017384), shZNF768_2 (TRCN0000017385). Lentiviruses were produced using psPAX2 and pMD2G as the packaging system. 293T cells were transfected with the vectors. Virus-containing supernatant was collected 48 h after transfection and filtered using a 0.45 µm filter. Cells were transduced for 24 h in the presence of 8 µg/mL polybrene. After the transduction, cells were dispersed into fresh medium. Cells were selected on the subsequent days using 2 µg/mL puromycin.All cells were rinsed twice with ice-cold PBS before lysis. Cells were lysed with Triton-X 100 containing lysis buffer . Cells were rotated at 4 °C for 10 min and then the soluble fractions of cell lysates were isolated by centrifugation for 10 min in a microcentrifuge. Protein levels were then quantified using Bradford reagent and analyzed by Western blotting. Protein extracts were diluted in sample buffer, denatured by heat (95 °C) for 10 min and loaded on precast gels . Proteins were transferred to PVDF membranes blocked in 5% milk diluted in PBS-Tween and incubated with their primary antibody overnight at 4 °C. The following antibodies were used: ZNF768 ; anti–β-actin . Secondary antibodies were purchased for Cell signaling technology . Amersham ECL Western Blotting Detection Reagent (RPN2106) was used to image the blots.n = 18), segmentectomy (n = 37), lobectomy (n = 270), pneumonectomy (n = 24), bilobectomy (n = 23) or another type of surgical resection (n = 2). A detailed summary of the cohort’s characteristics is presented in To characterize in detail the expression of ZNF768 in a large cohort of LUAD, immunohistochemistry (IHC) was performed on a tissue microarray (TMA) constructed using LUAD collected from 364 patients that underwent a wedge resection . Lastly, of the three cell lines, only the NCI-H441 is p53 mutant. We first identified two short hairpin RNA (shRNA) that effectively knockdown ZNF768  and one large cell carcinoma cell line (NCI-H460). All three cell lines are mutated for the K-RAS oncogene, while both the A549 and NCI-H460 cell lines contain mutations activating the PI3K/mTOR pathway , chromosome segregation , genome replication  and stability  were next measured. These genes were selected based on previous transcriptomics assays performed following the knockdown of ZNF768 in U87 cells [ZNF768 is a transcription factor that has been shown to regulate a wide variety of genes in a cell-specific manner by binding to mammalian-wide interspersed repeats (MIRs) in vitro . It was 87 cells  or the o87 cells . Some of87 cells . As showZNF768 is a newly characterized transcription factor that is required for cell proliferation ,14. We rZNF768 mRNA transcription [ZNF768 gene is rarely amplified in these tumors, these results suggest that post-transcriptional and/or post-translational processes probably take place to promote ZNF768 protein levels in cancer. Overall, these observations indicate that a disconnection between ZNF768 mRNA expression and ZNF768 protein levels exists in LUAD, and that measuring ZNF768 protein levels is key to study this transcription factor in this cancer.ZNF768 protein levels are elevated in LUAD. Our findings, that complement preliminary observations made in a smaller cohort of patients, also revealed a link between ZNF768 and proliferative markers in LUAD. As recently discussed, preliminary analyses through the Human Protein Atlas resource showed that several cancers display high ZNF768 protein expression . Autoantcription . BecauseZNF768 expression correlates with high proliferative clinicopathological features in LUAD. In cancer cell lines, ZNF768 promotes the expression of key genes required for proliferation ,14. SuppThere is growing interest in the study of the role of transcriptional regulators in cancer. Transcription factors, cofactors and chromatin regulators can contribute to tumorigenesis by altering the core transcriptional regulatory circuits of the cells to drive proliferation and survival ,19,20. HWe have previously reported that, in addition to control the expression of proliferative genes, ZNF768 also supports cell proliferation by directly repressing p53 . In vitrZNF768 expression is linked to proliferation in LUAD but is not a predictor for the overall survival of the patients in our cohort. These results suggest that ZNF768 is not a suitable prognostic factor in LUAD. Although this finding may look surprising, it is important to point out that tumour cell proliferation and survival are not always associated in cancer. For example, the link between the proliferation marker Ki-67 and survival is still controversial in some types of cancer and has limitations depending on the study ,29,30. FOur study suggests a possible role for ZNF768 in LUAD as a pro-proliferative transcription factor. Nevertheless, many questions remain concerning the exact impacts of ZNF768 in LUAD and its oncogenic potential. Foremost, the genomic context in which ZNF768 protein levels are elevated in LUAD is still unknown. Secondly, while in vitro evidence and cancer proteomics point to a possible role of ZNF768 in LUAD, additional studies are needed to define the exact contribution of ZNF768 in tumorigenesis. The use of animal models of cancer such as LUAD driven by oncogene(s) or xenografts with altered levels of ZNF768 could establish whether ZNF768 directly contributes to tumor formation in vivo. In parallel, it would be interesting to determine whether ZNF768 overexpression alone has oncogenic capabilities in vivo. Testing these hypotheses could help define the exact functions of ZNF768 in cancer and explore its therapeutic potential in this disease.In conclusion, we report that ZNF768 levels are commonly elevated and correlate with high proliferative markers in LUAD. We provide evidence that ZNF768 knockdown greatly impairs proliferation in NSCLC cell lines, which is accompanied by decreased expression of numerous genes involved in cell cycle progression and genomic integrity. These findings indicate that high levels of ZNF768 could offer a proliferative advantage to lung cancer cells and thus, might contribute to tumor formation. Further studies are required to establish the direct role of ZNF768 in human cancer and its possible mechanisms of action in vivo.


VT) is a standard of care, further individualization of VT may necessitate the evaluation of lung volume reserve . Low driving pressures provide a target for clinicians to adjust VT and possibly to optimize positive end-expiratory pressure (PEEP), while maintaining plateau pressures below safety thresholds. Esophageal pressure monitoring allows estimation of transpulmonary pressure, but its use requires technical skill and correct physiologic interpretation for clinical application at the bedside. Mechanical power considers ventilatory parameters as a whole in the optimization of ventilation setting, but further studies are necessary to assess its clinical relevance. The identification of recruitability in patients with ARDS is essential to titrate and individualize PEEP. To define gas-exchange targets for individual patients, clinicians should consider issues related to oxygen transport and dead space. In this review, we discuss the rationale for personalized approaches to mechanical ventilation for patients with ARDS, the role of lung imaging, phenotype identification, physiologically based individualized approaches to ventilation, and a future research agenda.A personalized mechanical ventilation approach for patients with adult respiratory distress syndrome (ARDS) based on lung physiology and morphology, ARDS etiology, lung imaging, and biological phenotypes may improve ventilation practice and outcome. However, additional research is warranted before personalized mechanical ventilation strategies can be applied at the bedside. Ventilatory parameters should be titrated based on close monitoring of targeted physiologic variables and individualized goals. Although low tidal volume ( Acute respiratory distress syndrome (ARDS) presents with a wide range of clinical and pathological characteristics , 2. MechFor clinicians, there is an understandable desire to standardize ventilatory management for patients with ARDS. Results from randomized clinical trials (RCTs) of interventions and strategies have been combined into meta-analyses to provide summary estimates of treatment effect to inform clinical practice and provide a starting point for a safe individualized approaches to mechanical ventilation . OutcomeSuggestion 1 Regulate the components of the ventilatory prescription based on close monitoring of targeted physiologic variables, intervention responses, and trends relevant to the integrated goals of treatment for the individual patient.VT) of 4–6 mL/kg predicted body weight (PBW) to keep plateau pressure (PPLAT) below 30 cmH2O] is the current standard of care [VT can be titrated to PBW, since lung volumes are correlated with the PBW. By contrast in ARDS patients, lung volumes do not correlate closely with PBW due to heterogeneous distribution of lung disease. Thus, VT should ideally be set according to end-expiratory lung volume (EELV) or inspiratory capacity (IC) measured at 30 cmH2O. At higher PEEP, EELV may change with respiratory system compliance (CRS), and setting of VT according to EELV is not reliable. Higher PEEP may not affect or even reduce IC, thus limiting further increases in VT. At lower PEEP, IC correlates more closely with EELV, and VT may be set by either IC or EELV [VT and mortality is stronger in patients with lower CRS, suggesting the importance of targeting VT in each patient according to the amount of aeration [VT, within safety ranges. For example, adaptive support ventilation, an automated closed-loop mode of ventilation, provides the best combination of VT and respiratory rate (RR), to achieve the lowest work of breathing combined with the lowest driving pressure (ΔP) and may outperform healthcare professionals with respect to VT titration [Lung protective ventilation [targeting tidal volume  has become a standard of care. The personalized targeting of VT may necessitate the evaluation of EELV or IC. Automated systems and artificial intelligence may enable better selection, monitoring, and control of optimal VT.P = VT/CRS) estimates VT adjusted to functional lung size and has been associated with mortality [P to titrate VT and/or PEEP in ARDS patients [CRS, ΔP is directly correlated with VT. At low CRS, a reduced VT is required to maintain ΔP within a safe range (< 13 cmH2O). Driving pressure may also be used to set PEEP since the best compromise between overinflation and recruitment is determined at the lowest ΔP [P was associated with lower mortality compared to increased PaO2/FiO2 [P including (1) depending on the VT used, the lowest ΔP may be achieved at different PEEP levels, (2) at higher CRS, compared to lower CRS, higher PEEP levels may achieve a lower ΔP, (3) the decrease in ΔP with PEEP may be associated with greater intratidal recruitment, (4) changes in chest wall compliance may affect the ΔP measurement, and (5) the presence of airway closure may confound the relationship between ΔP and PEEP. Although a causal effect has not been demonstrated, PPLAT > 29 cmH2O and ΔP > 19 cmH2O in moderate/severe ARDS patients have been associated with increased hospital mortality, irrespective of the PEEP and VT utilized [P during tidal ventilation has been gaining relevance recently and appears to hold potential for better guidance of protective mechanical ventilation.Driving pressure  and pleural pressure (PPL), with PPL estimated by esophageal pressure (PES) [PPL varies from non-dependent to dependent lung regions of the lung [PL gradient in the supine position primarily depends on lung weight as well as shape and mechanical properties of lung and chest wall. PES is a reasonable estimate of PPL in the zone between the non-dependent and dependent lung regions. In ARDS, the superimposed pressure from non-dependent to dependent lung regions is 10 cmH2O, on average [PPL is roughly PES + 5 cmH2O in dependent lung regions and PES-5 cmH2O in non-dependent lung regions. When interpreting PL from PES measurements, the absolute difference (not corrected) between PAW and PES at end-inspiration or end-expiration represents the PL in the middle lung, and the difference between end-inspiration and end-expiration in PPL (ΔPPL) approximates ΔPES. Elastance of respiratory system and chest wall may vary unpredictably and with changes in PEEP. In obese patients or those with increased intraabdominal pressure (PPLAT above 27 cmH2O), a simplified formula may help estimate the required correction of PPLAT: PPLAT target + /2 [2O and half of intraabdominal pressure is transmitted to the thoracic cavity [PL [PL (non-dependent lung) below 15–20 cmH2O; (2) ΔPL below 10–15 cmH2O; (3) PEEP set at end-expiratory PL (dependent lung) equal to 0–6 cmH2O; and (4) PL during recruitment maneuvers not to exceed 25 cmH2O [PL at end-expiration and compared with low or high PaO2/FiO2 table have not shown beneficial effects on outcomes [Transpulmonary pressure (PL), the distending force of the lung, is the difference between airway (re (PES) , 22. Durthe lung . The abs average ; thus, PcmH2O)/2 , 26. In vity [PL , 29: (1)25 cmH2O . To dateoutcomes , 32.Suggestion 4 In patients with increased intraabdominal pressure or morbid obesity, PL, may assist with individualizing ventilatory settings. The measurement of PES requires technical skills and physiologic interpretation to be applied by bedside clinicians.PRS is the change in respiratory system pressure, Raw the airway resistance. Equation 1 computes the changes of pressure from an undefined starting pressure, defining the change of the energy level stored in the respiratory system. Equation Mechanical power is defined as the amount of energy per unit of time and may vary within the span of an individual inflation or deflation half cycles by alteration in the flow profile . The mecentclass1pt{minimaP of 1 cm H2O to an already inflated lung (40 cmH2O of PEEP) produce a similar lung injury as when the same ΔP starts from 0 cm H2O PEEP? Supporters of the first equation of motion suggest that what really counts is the change. They claim that the energy required to climb one step on a staircase is the same regardless of which step one is starting from, as though the “rising the steps” occurs in a constant force field. In contrast, supporters of the second equation of motion claim that the force field varies with lung inflation. By analogy, it is as if a person climbed the stairs with an elastic band around the waist somehow anchored to the first step. Each step therefore requires more energy, and more energy results in more strain, and more strain results in more VILI. At present, there is a lack of agreement on the computation underpinning the calculation of mechanical power and determinants  that should be taken into consideration, and therefore, personalization is not possible. Although readily measured at the bedside, its value in VILI prediction remains controversial [A strong debate has ensued regarding PEEP and whether associated PEEP volume must be considered , 39 in toversial .Suggestion 5 Mechanical power is a summary construct that includes all of the important and well-recognized determinants of VILI. The same mechanical power value can be reached with different combinations of the above variables.PPLAT = 50–60 cmH2O) combined with higher PEEP titration increased patient mortality [PPLAT ≤ 28 cmH2O), but was stopped early as the intervention group experienced higher rates of new cardiac dysrhythmias. A recent meta-analysis showed that at low VT, the routine use of higher PEEP and/or RMs did not reduce mortality among unselected ARDS patients [VT and the energy transmitted to individual lung units. Conversely, if these approaches do not recruit significantly collapsed alveoli, then the increased airway pressures could cause overdistension of open lung with negative cardiovascular effects. Monitoring the response to lung recruitment maneuvers at the bedside, regarding their effect on apparent lung compliance, is critical. A bedside approach to estimate recruitability has recently been proposed by abruptly releasing PEEP (from 15 to 5 cmH2O) with an increase in expired volume [Ventilation strategies that target alveolar recruitment are based on the premise that a significant proportion of the volume loss within the ARDS “baby lung”  is due tortality . In contortality  tested apatients . In ARDSd volume . BrieflySuggestion 6 The identification of recruitability in ARDS patients, such as by the estimation of alveolar recruitment at bedside, is essential to personalize the use of recruitment strategies. However, systematic use of RMs is not associated with better outcome.2/FiO2-PEEP table has become a standard by which clinicians set PEEP. The largest trial investigating the effects of different ventilatory strategies in ARDS used a PaO2/FiO2-PEEP table to set PEEP. However, PaO2/FiO2 depends on FiO2 and PEEP levels affect cardiac output (CO). Recently, the concept of “keeping the lung at rest with permissive atelectasis and minimal oxygenation targets” has been proposed [2) is dependent on oxygen delivery (DO2); consequently, maximizing DO2 might be considered an alternative therapeutic goal for managing ARDS patients [2. In ARDS patients, it is important to individually maintain an adequate DO2 and hence adequate CO as opposed to targeting supra-normal values of DO2. Central venous oxygen saturation (ScvO2), as a reflection of the VO2/DO2 balance, may be a good marker of CO adequacy [2 uninterpretable [2 pressure (PCO2 gap) is useful since it is not affected by altered oxygen extraction. A PCO2 gap higher than 6 mmHg is indicative of inadequate DO2 and is increased in the presence of shock. Physiologic dead space is the portion of each VT that does not take part in gas exchange and represents a good “global index” of the efficiency of the lung function being strongly associated with outcome and helpful for PEEP setting [ETCO2/PaCO2 ratio, though less precise, appear to be an excellent surrogate for physiologic dead space [The use the PaOproposed . Oxygen patients , 51. Botpatients  and RCTspatients , 54 haveadequacy . Howeverpretable . In thes setting , 58. Howad space , 60.Suggestion 7 Gas exchange, including oxygenation, is a widely used parameter to set mechanical ventilation in ARDS. Physiologic dead space and the ventilation ratio should be also considered, taking into account the balance between aeration and perfusion. The measurement of PCO2 gap is important to individually identify inadequate DO2.Different imaging techniques have been proposed to personalize mechanical ventilation strategies. Lung ultrasound (LUS) and electric impedance tomography (EIT) may be performed at bedside, while chest computed tomography (CT) scan require transportation of patients outside of the intensive care unit (ICU). Chest CT scan allows the detection of relevant parenchymal alterations including the amount and regional lung distribution of hyperinflated, aerated, and non-aerated lung tissue . Two maiDual-energy CT scan has been recently used to investigate ventilation perfusion relationships at different ventilation settings in COVID-19 patients . LUS hasSuggestion 8 Imaging techniques may help to better identify different lung morphology and response to ventilation strategies. Chest CT allows detailed and quantitative analysis of overdistended lung: normally aerated, poorly aerated and non-aerated tissue, as well as consolidated and atelectatic components. LUS and EIT are promising tools for clinicians to use at the bedside.ARDS is characterized by different pathogenetic pathways leading to similar clinical presentations postulated to represent distinct phenotypes, which may enable precision therapy. To date, two different ARDS phenotypes (hyperinflammatory and hypoinflammatory) have been identified that differ in response to therapy and outcomes. Post hoc latent class analysis of a panel of blood biomarkers for inflammation, endothelial injury, and coagulopathy combined with clinical variables has revealed two phenotypes in 5 RCTs –80. SimiSuggestion 9 The stratification of ARDS patients according to different phenotypes is promising but awaits clinical confirmation before it can be translated into to the clinical setting.2O and FiO2 = 0.6 to maintain a PaO2 = 60 mmHg, increasing PEEP to 14 cmH2O may achieve the same PaO2 at an FiO2 = 0.5. Some clinicians may see that as physiological success. However, apportioning “value” to this increase in PEEP prioritizes lung mechanics and lung physiology because they can be measured. However, the impact of the increased PEEP on renal function, gastrointestinal permeability, or the brain typically remains unmeasured and unknown. This is a fundamental problem with physiology: it measures whether a specific intervention affects a specific set of physiological parameters. However, it does not assess “other” unmeasured or unintended physiological consequences. Thus, physiologically based personalized therapy, although clearly important at the extremes of illness, become problematic at intermediate levels of illness where safety has typically been established. Historically, the pursuit of perceived physiological success in the belief that it would lead to subsequent clinical success has often proved to be disappointing with notable examples including intensive insulin therapy to normalize glycemia [In daily practice, critical care physicians use physiological data to aid them in guiding and adjusting therapies. However, clinicians are not able to accurately predict the medium- or long-term consequences of physiologically based therapy on patient-centered outcomes. Clinicians often fall prey to celebrating immediate physiological gains under the pretense that they will translate into improvements in desired outcomes. In this regard, physiological gains can be seductive. When these gains are achieved, clinicians feel validated (immediacy bias) regardless of the downstream future effects which are unknown. When anticipated gains are not achieved, clinicians engage in attribution bias-blaming severe and unresponsive illness. Short-term physiological gains are an elusive concept. For example, in an ARDS patient on PEEP = 8 cmHglycemia ; drotrecglycemia ; colloidglycemia ; decomprglycemia ; hypotheglycemia , early pglycemia ; glutamiglycemia ; fluid bglycemia . All thiSuggestion 10 When applying physiological manipulations, clinicians should consider the uncertainty surrounding their subsequent effect on patient-centered outcomes.PL; and (3) identification of patient’s phenotype according to the biomarkers of epithelial and endothelial cell damage, inflammation, and extracellular matrix. The investment of personalized mechanical ventilation is high and will require investment of both personnel and resources, including experimental and clinical trials.Personalized mechanical ventilation in ARDS merits further research on specific targets: (1) characterize biomarkers profiles and responses to specific treatments associated with pulmonary or extrapulmonary insults, pulmonary inflammation, and lung physiology; (2) the use of mechanical power and A personalized mechanical ventilation approach based on lung physiology and morphology, ARDS etiology, lung imaging as well as identification of biological phenotypes may improve and individualize future mechanical ventilation practice. Additional research is warranted before personalized mechanical ventilation strategies can be applied at the bedside of ARDS patients.


Despite the big increase in precision medicine targeted therapies developing curative treatments for many cancers is still a major challenge due mainly to the development of drug resistance in cancer stem cells. The cancer stem cells are constantly evolving to survive and targeted drug treatment often increases the selective pressure on these cells from which the disease develops. Chronic myeloid leukaemia is a paradigm of cancer stem cell research. Targeted therapies to the causative oncogene, BCR/ABL, have been developed but drug resistance remains a problem. The introduction of tyrosine kinase inhibitors targeting BCR/ABL were transformative in the management of CML. However, patients are rarely cured as the tyrosine kinase inhibitors fail to eradicate the leukaemic stem cell which often leads to loss of response to therapy as drug resistance develops and progression to more fatal forms of acute leukaemia occurs. New treatment strategies targeting other entities within the leukemic stem cell either alone or in combination with tyrosine kinase are therefore required. Drawing on our previous published work on the development of potential novel targets in CML and other myeloproliferative diseases along with analysis of the facilitates chromatin transcription (FACT) complex in CML we hypothesised that curaxin, a drug that targets the FACT complex and is in clinical trial for the treatment of other cancers, could be of use in the treatment of CML. We therefore assessed the curaxin CBL0137 as a new agent to extinguish CML primitive cells and show its ability to preferentially target CML cells compared to healthy control cells, especially in combination with clinically relevant tyrosine kinase inhibitors. BCR/ABL-related or -unrelated mutational events leading to drug resistance and/or a differentiation blockade (manifesting as blast crisis). The Failure of TKIs to eliminate the LSCs, plus the development of drug resistance and transformation to a more fatal disease make CML an exemplar of cancer stem cell research highlighting the difficulties encountered in precision medicine as applied to cancer treatment. It has been argued that CML cannot be cured by TKIs alone but what is needed are new drugs exploiting differences between CML and normal haematopoietic stem cells [The oncogene BCR/ABL is the hallmark of Chronic Myeloid Leukaemia (CML) . The inem cells .Curaxins are being used successfully in other leukemias and solid tumours to combat drug resistance . Curaxins target the facilitates chromatin transcription (FACT) complex and are reported to activate p53, inhibit NF-kβ and be involved in MYC driven transcription ,14. Quin+ cell population were enriched using CliniMACS (Miltenyi Biotec) according to standard protocols and previously described [Written informed consent was obtained for all samples and all research was in compliance with the ethical and legal framework of the Human Tissue Act. Experiments had ethical approval from the NRES committee of the regional NHS health research authority (14/LO/0489 and 17/LO/0888). Primary CML samples were obtained from the Manchester Cancer Research Centre Biobank (HTA 30004) following authorisation by the Tissue Biobank’s scientific sub-committee. Control samples were surplus cells isolated from Leucocyte cones from healthy individuals undergoing leukapheresis during cell donation within the NHS Blood and Transplant Service. The CD34escribed .Protein expression was assessed using flow cytometry with the Novocyte flow cytometer (ACEA Biosciences) using FloJo software or western blot analysis using standard protocols as previously described . Cellula4/ml were plated in triplicate into 96 well cell culture plates and drug or carrier control added. Following incubation for 24 or 48 hours at 37°C in 5% CO2 10μl of WST-1 reagent was added. After 3 hours incubation with the WST-1 reagent absorbance was measured at 450nm on a 800TS microplate reader (BioTek). Colony forming assays on primary patient samples were performed in methylcellulose complete media (R&D systems) supplemented with 2u/ml EPO with CD34+ cells at a density of 3000cells/ml as previously described [K562 cell proliferation was measured using WST-1 reagent (Roche Diagnostics). 100μl of K562 cells at 2 x10escribed . To asseThe two components of the FACT complex (SSRP1 and SUPT16H) are up-regulated at the mRNA level in quiescent CML stem/progenitor cells  S1 Fig)S1 Fig). As correlation between mRNA and protein levels in oncogene expressing cells is limited ,25 we coIn concordance with Affer et al , who obsTo identify an effective dose range for the curaxin CBL0137 in BCR/ABL expressing cells we undertook proliferation assays utilising the cell line K562 in the presence of CBL0137 and compared its inhibitory ability to the frontline CML drug Imatinib. Following 24 hours  treatmenHaving established that the targets of curaxin are perturbed at the protein expression level in CML  and idenie was there an effect on self-renewal  and issues with the development of drug resistance approaches other than targeting BCR/ABL have been proposed . FurtheHaving demonstrated the ability of CBL0137 to preferentially kill primitive CML cells we investigated its potential mechanism of action. Curaxins have been reported to target the FACT complex and act via p53, NF-kβ, MYC and β-catenin in other cancers . HoweverAn effect we observed following 18 hours CBL0137 treatment  was a deTo further investigate the effects of CBL0137 on β-catenin activation we enriched nuclear and cytoplasmic fractions from K562 cells treated with CBL0137 and undertook western blot analysis. CBL0137 treatment leads to a nuclear translocation of β-catenin  suggestiOur preliminary observations demonstrating the enhanced inhibition of CML colony formation, induction of DNA damage and interference of β-catenin signalling highlight the potential utility of CBL0137, a well-tolerated drug, for the treatment of CML either alone or in combination with TKIs. Given the fact that curaxins are already proving their worth in the treatment of other malignancies and the proposal that a combination of β-catenin inhibitors and TKIs may be useful to eradicate leukaemic stem cells our study provides evidence to support the initiation of further studies on the use of curaxin in CML treatment.S1 FigJ Oncol. 2011;2011:798592–798592). Affer et al employed Hoechst 33342 and Pyronin Y to enrich CD34+ cells in GO from patients in chronic phase CML and healthy controls. RNA measurements were made utilising Affymetrix Human Genome U133 plus 2.0 arrays. Results are displayed as mean+/-SEM of log2 expression intensity . SSRP1 shows on average a 3.4 fold increase in expression at the mRNA level in CML quiescent cells compared to quiescent cells isolated from healthy controls. The results of a t-test are shown **<0.01.Data extracted from Affer et al ((DOCX)Click here for additional data file.S2 FigComplete images for the western blots shown in (PDF)Click here for additional data file.


Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is emerging as a valuable tool for non-invasive volumetric monitoring of the tumor vascular status and its therapeutic response. However, clinical utility of DCE-MRI is challenged by uncertainty in its ability to quantify the tumor microvasculature ( This network is abnormal and malformed with leaky and tortuous vessels in comparison to normal tissue. The malformed nature of the tumor vasculature often results in poorly oxygenated or hypoxic regions which are more likely to metastasize and are also more resistant to common cancer treatments such as radiation therapy (RT) and chemotherapy. Such characteristics of the tumor vasculature are typically highly heterogenous spatially  depends on microvascular ablation in addition to tumor cellular DNA damage as part of its mechanism of action7.Given the strong dependence of tumor cell survival and treatment resistance on the vascular network, there has been a significant effort to develop new cancer treatments that target the tumor vasculature. For example, in the radiation oncology field, clinicians have explored the administration of a vascular endothelial growth factor (VEGF) blockade with the goal of ‘normalizing’ the vasculature to improve oxygen and nutrient delivery before RT9. Non-invasive imaging of the detailed structure and function of the tumor vasculature may facilitate therapy personalization during the treatment planning stage, rapid adaptation throughout the course of treatment, and prognosis post-treatment9.Despite the promising early results of these therapies, optimal scheduling and dosages for concurrent administration with conventional treatment modalities (such as chemotherapy and radiation therapy) has yet to be realized14 and selected clinical16 studies of the vasculature. Speckle variance optical coherence tomography (svOCT) is a functional extension of conventional structural OCT that allows for high resolution imaging of tumor microvasculature10. svOCT has advantages over many other intravital microscopy techniques in that it does not require exogenous contrast agents, can image the vasculature in 3D to a depth of 1–3 mm, and is relatively cheap and fast. Most importantly, svOCT’s vascular contrast is excellent and its resolution affords capillary imaging. svOCT has been used in studies to quantify differences between normal and tumor vasculature11 as well as to study the response of the vasculature to radiation15 and photodynamic therapies16. Despite its excellent ability to visualize and quantify the volumetric maps of tissue blood vessels down to the capillary level and selected therapy-monitoring applications in patients16, clinical implementation of this technology in a wide variety of anatomical sites is hindered by its shallow penetration depth17. An alternate imaging modality with deep-tissue vascular imaging capabilities must be considered for wider clinical implementation.Optical microangiography plays an important role in preclinical20. DCE-MRI involves the bolus administration of a gadolinium (Gd) contrast agent followed by a time series imaging of its tissue accumulation and clearance. Analysis of Gd contrast enhancement kinetics can offer insight into the function, and perhaps underlying structure of the vasculature24, and its response to vascular targeting therapies20.Magnetic resonance imaging (MRI) is already prevalent in cancer care, given its excellent soft tissue contrast and ability for large field-of-view imaging in 3D with excellent penetration depth and avoidance of harmful ionizing radiation. Cancer therapies can in principle be monitored longitudinally via MRI biomarkers that report on tumor treatment response, including using perfusion-sensitive techniques such as dynamic contrast enhanced (DCE) approaches17. To address this concern, preliminary studies have compared DCE-MRI parameters with histology24 and intravital microscopy14, and have found some correlations between these modalities. However, both histological comparisons24 and intravital microscopy studies14 suffer from poor co-registration with MR images and are limited by their simple 2D analysis of the vascular network. Here we attempt to fully analyze and address the ‘macro-to-micro resolution gap’ by directly correlating high resolution 3D in-vivo volumetric ‘ground truth’ svOCT images of the tumor vasculature with semi-quantitative and quantitative vascular metrics derived from lower resolution MR-contrast enhancement time series imaging in the same animals. Through these correlations, we hope to determine whether microvascular information can be gleaned from coarser-resolution DCE-MRI datasets, thereby increasing its available information content and potentially enhancing its clinical value.Despite the promise of DCE-MRI for predicting and monitoring cancer treatment response, questions have been raised on its ability to accurately assess and quantify the tumor microvasculature (nullIL2r null (NRG) mouse strain was chosen for this study. Seven mice were subcutaneously inoculated with ~after tumor inoculation so that the initial tumor growth dynamics and its microvascular development were not altered25. The window chambers had eight ‘divots’ on the top surface for placement of fiducial markers  to facilitate MRI to svOCT image co-registration . The reporting in this manuscript follows the ARRIVE guidelines. The radioresistant and immunocompromised NOD-Rag126 allowing for acquisition of the full complex interferometric signal to suppress the complex conjugate artifact. The sample arm of the quadrature interferometer contained a semiconductor optical amplifier (SOA) with a gain of 35 dB to boost the back reflected signal from the tissue. The SOA had the same center wavelength and bandwidth as the laser source . Polarization controllers were used to minimize the differences between the shape of normalized light spectra in the reference arm and after the SOA. The A-scan rate of the OCT imager was 20 kHz. Two detector outputs were digitized using a data acquisition card with 16-bit resolution and sampling rate of 250 MS/s. The resultant axial and lateral resolutions in air were 8 OCT images were acquired using a previously described swept source OCT system based on a quadrature interferometer2 field of view (FOV), by stitching together laterally adjacent 2 scans. The subdivided acquisition of the FOV reduces bulk tissue motion artifacts by decreasing the amplitude of motion of the OCT’s scanning-mirror galvanometer, thus reducing the inter-frame time (the time between scan repetitions at a given location). Each B-scan consisted of 400 A-scans and was performed 8 times per location (25 ms apart) to enable speckle variance processing10. This repeat sequence and temporal spacing were previously determined to result in sufficiently high speckle variance SNR and fast decorrelation in blood microvasculature compared to other physiological motions of surrounding tissue27. Hence enhanced contrast for image voxels containing blood is obtained via interframe speckle variance calculations. Tumor cell brightfield :OCT data were analyzed using in-house built software written in MATLAB R2020A . Speckle variance processing was used to obtain 3D images of the tumor microvasculatureet al11  was defined as the proportion of vessels within the analyzed volume. A 3D Euclidean distance transform was then applied to the entire segmented vascular volume to obtain a histogram of the distances to the nearest vessel (DNV). The mean distance to the nearest vessel , equipped with the B-GA12 gradient coil insert and 7.2 cm inner diameter quadrature cylindrical RF coil. Each mouse was anesthetized and positioned on a slider bed with inlaid tubes circulating water warmed to 38 Imaging consisted of orthogonal 2D 28. The effective For DCE-MRI analysis, the raw MRI signal enhancement was converted to gadolinium concentrations for each imaging timepoint (29. The maximum enhancement (ME) was the maximum Gd concentration reached by the given voxel over the DCE-MRI time course. The time-to-peak Gd concentration (TTP) was defined as the time from initial contrast agent arrival in the voxel to the time at which maximum Gd concentration was reached. The wash-in rate (WIR) was calculated as the slope of the line connecting the point of initial contrast agent arrival in the voxel to the point of maximum Gd concentration. The area under the curve (AUC) was calculated over 15 min from initial contrast agent arrival in the voxel.Semi-quantitative analysis was performed on the resulting Gd time-concentration curves for each voxel30 to the Gd time-concentration curves for each voxel. This approach allowed for direct estimation of 30. A biexponential population-based AIF for mice proposed by Benjaminsen et al21 was used,To progress beyond the empirical concentration curve analysis above, a variety of compartmental models have been proposed in the medical imaging community that offer more biophysical insight through the model fitting parameters. In this context, we performed a non-linear least-squares fitting of the Toft’s modelet al21. The rate constant for Gd moving from the EES to the intravascular space . For intermodality correlations, the DCE-MRI metrics for each position of the sliding window VOI were plotted against the corresponding svOCT measurements for the VOI. The Spearman’s correlation coefficient  than healthy mice, indicating impaired vascular development consistent with the literature37. On the other hand, tumor bearing mice had a shallower wash in slope along with decreased ME and a longer TTP indicating decreased perfusion compared to healthy mice37.A statistically significant difference between healthy and tumor tissue was found for all semi-quantitative DCE-MRI metrics Table A and B a30. Therefore, the larger The Toft’s model fit to the Gd time concentration curves are shown as the red line in Fig. Overall, comparison of healthy and tumor tissue showed significant differences in microvascular structure (as shown by svOCT) as well as differences observed on DCE-MRI contrast enhancement curve shape and fully quantitative tissue parameters. The delayed contrast enhancement with shallower wash in rate and decreased maximum enhancement is consistent with impaired vascular development in tumors compared to healthy controls. Fully quantitative metrics displayed significant differences in macrometrics may offer the most insight into the underlying tissue microvasculature as reported by svOCT, a To determine which DCE-MRI entclass1pt{minimaFirstly, the magnitude of Spearman correlation coefficients between DCE-MRI metrics and svOCT metrics  implies decreasing intervascular spaces  Fig. A and B. 24. A negligible correlation was found for semi-quantitative parameters, a high correlation was found for TTP with VVF and 23. A moderate correlation was found for WIR with VVF and Moving on to microvascular svOCT correlations with the DCE-MRI th) Fig. C and D. Note that the numerator in Eq.  is ME si39, which may explain these findings. Changing the time over which AUC is integrated over may also lead to improved correlations with svOCT microvascular metrics.Interestingly, low to moderate correlations were noted for the semi-quantitative MRI metric, AUC , TTP is approximated by:The highest correlation identified in our study was TTP with svOCT microvascular metrics VVF and th) Fig. C and D. th) Fig.  into Eq.th) Fig. . Setting30. In combination with Eq.  that scale as the square of the DNV: DNV2/D, where D is the agent diffusivity34. In moving from micron (OCT) to mm (MRI voxel) scales, this square dependence enhances the contribution of the aperfused regions (large DNV) in the voxel average. Conversely, the mean DNV  suggestd in Eq. : for an ex-vivo analogue of our in-vivo 3D VVF metric24. These studies are limited by their 2-dimensional analysis of vascular density using histological preparations which are also subject to uncertainty related to the number and location of histological sections taken from the tumor. Others report confocal microscopy images of the tumor vasculature to compare with DCE-MRI parameter maps14 but these are also limited by their 2-dimensional analysis of the vasculature. Our advanced svOCT imaging platform allows for high resolution 3D images of microvasculature in-vivo. This provides a more accurate assessment of the VVF and 47. Our ability to co-register these 3D svOCT vascular images with DCE-MRI datasets in-vivo in the same animals adds an “extra level of realism” to these studies. The spatial correlations that we have performed are important for demonstrating the ability of DCE-MRI to differentiate between regions in the same tumor that have different vascular microstructures. This may prove useful in several clinical scenarios including radiotherapy applications where conformal doses can be created to selectively boost or avoid specific regions of the tumor (i.e. dose painting)48. For example, conformal radiotherapy plans may be created using these spatial DCE-MRI parameter maps to selectively avoid poorly vascularized tumor regions . This made it challenging to define the tumor boundary below the surface of the tissue, especially in situations where there was exudate buildup in the window chamber. To reduce this uncertainty, future work will implement a 3D tumor segmentation algorithm we are currently refining that is more objective In conclusion, we have performed in vivo 3D microvascular imaging using high resolution svOCT and comparably lower resolution DCE-MRI via dual-modality-compatible window chamber mouse xenograft tumor model. Our goal was to use the detailed volumetric visualization afforded by svOCT to increase the information content derivable from clinically measurable DCE-MRI metrics, towards understanding / optimizing / guiding feedback-driven adaptive vascular targeting therapies. We thus performed co-registered spatial correlation analysis between svOCT microvascular descriptors (VVF and Supplementary Information.


This cross-sectional study examines fluoride varnish application rates during well-child medical visits and identify characteristics associated with fluoride varnish receipt. Studying children who are privately insured is important because coverage of this service without cost-sharing has been mandated since 2015,5 and fewer than 1 in 3 children under 5 years who are privately insured have an annual dental visit.6 We used data from 4 states to examine fluoride varnish application rates during well-child medical visits and identify characteristics associated with fluoride varnish receipt.Fluoride varnish is effective at reducing tooth decay, which affects nearly a quarter of US children ages 2 to 5 years and more than half of children ages 6 to 8 years.STROBE) reporting guideline.This cross-sectional study was approved by RAND’s institutional review board, and a waiver of informed consent was granted because of the use of deidentified patient data. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology  during well-child visits with the same service date.P < .05, and we used county-level cluster robust standard errors. Data analyses were performed using SAS Version 9.4 and Stata-MP version 16.1 (StataCorp). Analysis was performed during November 2020 to March 2021.We calculated descriptive statistics and estimated the unadjusted and adjusted odds of a visit including fluoride varnish using logistic regression; we then calculated regression-adjusted probabilities of fluoride application  Of the visits, 134 662 (41.0%) occurred in Connecticut and 15 756 (4.8%) included fluoride varnish applications . Fluorid6 and delivering fluoride varnish in medical settings can increase access to preventive oral health services. Although this study was limited to 4 states, these data included a variety of private insurers and clinicians. Differences in fluoride varnish applications across states may be driven by variation in access to dentists and Medicaid payment policy. This study was the first to assess delivery of an evidence-based service for children who are privately insured, which is recommended by the US Preventive Service Task Force and the American Academy of Pediatrics. Although increases over time were encouraging, very low rates of fluoride varnish in medical settings suggest substantial expansion of this service in medical settings is critical for improving children’s oral health and overall well-being.Despite mandatory insurance coverage for fluoride varnish applications in medical settings, we found fewer than 5% of well-child visits for privately insured young children included this service, suggesting efforts are needed to increase pediatric medical providers’ delivery of fluoride varnish. Young children have few dental visits


Demyelination of the central nervous system is a prominent pathological hallmark of multiple sclerosis and affects both white and grey matter. However, demyelinated white and grey matter exhibit clear pathological differences, most notably the presence or absence of inflammation and activated glial cells in white and grey matter, respectively. In order to gain more insight into the differential pathology of demyelinated white and grey matter areas, we micro-dissected neighbouring white and grey matter demyelinated areas as well as normal-appearing matter from leucocortical lesions of human post-mortem material and used these samples for RNA sequencing. Our data show that even neighbouring demyelinated white and grey matter of the same leucocortical have a distinct gene expression profile and cellular composition. We propose that, based on their distinct expression profile, pathological processes in neighbouring white and grey matter are likely different which could have implications for the efficacy of treating grey matter lesions with current anti-inflammatory-based multiple sclerosis drugs. et al. present gene expression data of demyelinated and normal-appearing white and grey matter of multiple sclerosis leucocortical lesions. They find that grey matter lesions primarily feature gene expression indicative of a microglial response while white matter lesions feature astrocytic activation and dystrophic neurons.van Wageningen   Post-mortem analyses revealed that demyelination in the white matter is accompanied by activation of local glial cells and infiltration of peripheral leucocytes, including monocytes and lymphocytes. In contrast, demyelinated grey matter areas show a paucity of activated glial cells and little to no infiltration of peripheral leucocytes.2–4 Several hypotheses have been put forward as to why demyelinated white and grey matter differ in their pathology, including (i) the presence of neuronal cell bodies, suppressing an immune response in the grey matter,6 (ii) a difference in the abundance of myelin eliciting an immune response7 or (iii) that grey matter pathology is largely driven by the presence of meningeal infiltration of B-cells instead of parenchymal infiltration of leucocytes, as is observed in the white matter.8–10 Thus far, analyses of brain material from multiple sclerosis patients, suggest that, irrespective of the presence of infiltrating leucocytes, glial cell activation, chronic oxidative injury and accumulation of mitochondrial damage in axons are important contributors to the pathological processes in white matter lesions (WMLs) ultimately leading to axonal loss.1 Similarly, astrocyte scarring, represented by hypertrophic astrocytes and deposition of extracellular matrix proteins, is present in the demyelinated white matter only.12 In contrast, in the grey matter, the role of glial cells, including microglia and astrocytes in multiple sclerosis pathology, is largely unknown, but glial cell reactivity could be affected by CSF or meningeal-derived inflammatory factors, e.g. complement or cytokines.13–15 The direct relation between inflammation in meninges or CSF and cortical demyelination is, however, at least questionable.17Multiple sclerosis is a neurological disorder pathologically characterized by inflammation, demyelination and axonal damage in the CNS. It is the most common immune-mediated neurological disease among young adults, clinically represented by heterogeneous symptoms including sensory and motor deficits as well as fatigue and cognitive dysfunction.18) and in mouse models of multiple sclerosis (including but not limited to)19–21 sequencing data of multiple sclerosis (demyelinated) grey matter is relatively sparse. Until now micro-array data of multiple sclerosis normal matter22 have appeared. Although of interest, none of these studies looked into gene expression profile differences between grey and white matter multiple sclerosis lesions compared with normal-appearing grey matter (NAGM) and NAWM. In addition, the focus of most of these grey matter expression studies is on subpial demyelination only. We consider the comparison of gene expression profiles of pathologically confirmed demyelinated white and grey mater material compared with its respective normal-appearing matter of considerable importance. Particularly, because current drug treatment of multiple sclerosis patients focuses on reducing the inflammatory components of the disease, e.g. less influx of lymphocytes as occurs in demyelinated white matter only. In demyelinated grey matter, the relative absence of these cells may point to another, less inflammatory, mechanism underlying demyelination, which may impact upon therapy development. In the present study, we thus analysed demyelinated white and grey matter from the same histologically verified post-mortem human leucocortical multiple sclerosis lesion and normal-appearing tissue blocks by RNA sequencing to generate transcriptomic data. Using these data, we address the gene expression profile of (demyelinated) grey matter and (demyelinated) white matter and compare these to each other. In addition, we assess the effect of different cellular compositions in (demyelinated) white and grey matter and the effect on the gene expression profile and enriched pathways.To date, the clear difference in pathological appearance and molecular mechanisms underlying demyelinated grey and white matter multiple sclerosis lesions have not been backed up with a more in-depth analysis that may guide understanding of pathways involved. Though there have been several studies investigating gene expression in demyelinated and normal-appearing white matter (NAWM) in humans  human brain tissue blocks were obtained from the Netherlands Brain Bank and the Amsterdam Multiple Sclerosis Centre, Amsterdam UMC and were selected from material available from the years 2003 to 2019. The inclusion criteria were (i) availability of both frozen and FFPE tissue, (ii) clear loss of proteolipid protein (PLP) immunoreactivity encompassing neighbouring white and grey matter, (iii) amoeboid, activated major histocompatibility complex II (MHC-II) positive cells within the demyelinated white matter of the lesion. In practice, we observed that whereas sometimes there was a leucocortical lesion present in the collected FFPE material, this was not always the case in the frozen material. Therefore, at a later time point, we also included leucocortical lesions and concomitant normal-appearing matter present in frozen or FFPE only material. In compliance with all local ethical and legal guidelines, informed consent for brain autopsy and the use of brain tissue and clinical information for scientific research were given by either the donor or the next of kin. Clinical information can be found in 2O2 in TBS to block endogenous peroxidase, washed in TBS and incubated for 30 min in 5% donkey serum in TBS with 0.1% Triton-X (block buffer) to block non-specific antibody binding. Thereafter, sections were incubated with primary antibodies . Cryo-sections were fixed in 100% acetone for 45 min, dried and washed in 0.1 M Tris-buffered saline , whereas FFPE sections were deparaffinized before undergoing antigen retrieval using 10 mM Tris buffer containing 1 mM ethylenediaminetetraacetic acid . Subsequently, all sections were incubated for 20 min with 1% Hdies see  diluted Double-labelling immunohistochemistry was performed on FFPE sections treated similar to that described above until the incubation with primary antibodies. Sections were incubated with combinations of primary antibodies diluted in block buffer, overnight at 4°C . Section24 In short, LPR-stained cells (red) and Vector SG-stained cells (grey) were separated based on their light emission, which yields images similar to fluorescently labelled antibodies.25 Using the open-source software ImageJ,26 compositions of the separated signals were then made to visualize co-localization  and were analyzed as previously described in van Wageningen et al.tion see .27 with slight modifications. In short: fresh-frozen tissue blocks were sectioned at 10 µm in an RNAse-free cryostat and mounted on PEN slides (Leica). Immediately after mounting, the sections were fixed in 100% molecular grade ethanol for 10 min and dried. Subsequently, the sections were stored at −80°C, paired back to back in a 50 ml tube containing silica gel as a desiccant. Upon use for LCM, sections were taken from storage and immediately put into 100% molecular grade ethanol, rehydrated to 70% ethanol and then stained in 4% cresyl violet in 70% ethanol for 45 s. After staining, sections were dehydrated in an ethanol series (up to 100% ethanol). Sections were dried and then stored again in 50 ml tubes containing silica gel on dry ice before commencing LCM. Upon use for LCM, frozen, cresyl violet-stained sections on PEN slides were transferred to 100% ethanol for 1–2 min and dried. NAWM and NAGM and demyelinated white and grey matter were localized and dissected using the Leica LMD6500 system . Per section, three squares of tissue per area were captured. Per patient, tissue was captured from 6 to 12 cresyl violet-stained sections. We repeated this procedure for subsequent quantitative polymerase chain reaction (qPCR) analysis using tissue from a subset of the same patients used for RNA-seq and two additional patients (see Sample preparation for laser capture microscopy (LCM) was done as previously publishedents see .® PicoPure® RNA Isolation Kit (ThermoFisher Scientific), incubated at 42°C for 30 min (according to the manufacturer’s protocol) and stored at −80°C until further use. RNA was isolated according to the manufacturer’s protocol with an additional DNAse digestion step using the Qiagen RNAse-free DNAse kit . RNA quality and concentration of all samples were determined using the Agilent Bioanalyzer before and after the LCM procedure.Pooled laser-captured samples per area per patient were collected in 50 µl extraction buffer of the ARCTURUSIn total, 11 frozen tissue blocks from 11 patients with multiple sclerosis featured a Type I leucocortical lesion that fitted the previously established pathological criteria. The mean ± standard deviation (SD) RNA integrity indicated by the RNA integrity number (RIN) score of the tissue blocks before the LCM procedure was 6.2 ± 1.2. After the LCM procedure, the mean ± SD RIN value of laser-captured samples was 5.1 ± 1.2 which was considered appropriate to be used for RNA sequencing. For additional qPCR, we obtained two more frozen tissue blocks with leucocortical lesions from two additional patients. However, we could not include all the tissue blocks used for RNA-seq, a second time for qPCR as we had no left over material from some blocks. We repeated the experimental procedure to generate RNA samples for qPCR, tissue used for this experiment can be found in Sequencing libraries were prepared with the Quant Seq 3′ mRNA seq Library Prep Kit FWD . The libraries were sequenced on a NextSeq platform.Homo Sapiens (GRCh38.92) reference template obtained from Ensembl and quantified with featureCounts. A quality check of aligned data was performed with FASTQC and MultiQC. Raw count matrices were loaded in R and annotated by converting the ensemble IDs to gene symbols using the corresponding gtf file. Only genes with >1 counts in at least two samples were included in the analysis.Quality control of the raw FASTQ files was performed with FASTQC. Bad quality bases were trimmed with TrimGalore version 0.4.5. Sequences were aligned using HiSat2 version 2.1 to the EdgeR package. A likelihood ratio test (LRT) was used to compare gene expression between the four defined groups using only tissue area as a variable (i.e. NAWM and NAGM and demyelinated white and grey matter). Subsequently, the DEGpatterns package was used to divide the differentially expressed genes into clusters based on gene expression pattern. The threshold for each cluster was set at a minimum of 50 genes. Up- or downregulated genes were identified with the criteria of a logFC >(−)2 and a P-value of <0.05. Pathway analysis of genes represented in the clusters was conducted using the g:Profiler package.Subsequently, data were normalized and analysed using the 28 with single-cell data generated from frozen multiple sclerosis (demyelinated) white and grey matter by Schirmer et al.22 Data were downloaded from the Sequence Read Archives from the accession number PRJNA544731 (NCBI Bioproject ID: 544731).To determine the cell type specificity of genes, we used CIBERSORTxhttp://www.hfrc.nl] using the following calculation N0 = Nq/ECq . Housekeeping genes were selected based on expression as observed in the RNA-sequencing results. MRIP and SDHA showed minimal variation between samples and between groups and thus the geomean of these two genes was used for data normalization  with random primers  according to the manufacturer's description. Semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR) was performed in a total volume of 10 µl per sample consisting of 3 µl of Power SYBR Green Master Mix , with 50 µM of each forward and reverse primers see  and 6 nglization . Data an2) was determined. CD11c+ cell counts were conducted by setting a threshold using the ‘Auto Threshold’ function and subsequently counting the positive cells by using the ‘Analyze Particles’ plug-in, setting the size at 10 µm2. Analysis of CD11c+/Iba-1+ cells was conducted by first selecting Iba-1+ cells using the Auto-threshold function and subsequently counting the CD11c+ cells using the ‘Analyze Particles’ plug-in within the selected Iba-1+ cells. GFAP and PLSCR4 immunoreactivity were analysed by first setting a threshold using the ‘Auto Threshold’ plug-in and subsequently measuring the per cent of area stained.Per area (NAWM or NAGM and demyelinated white or grey matter) two representative images were taken of which cell counts were determined. The mean cell count per region of interest (ROI) (0.36 mm+ microglia were traced in 3D in 10 µm thick sections using a Leica DMR HC microscope coupled with Neurolucida tracing software (MBF Bioscience). The ROI was determined by the area of demyelinated grey matter and NAGM in PLP-stained sections. These ROIs were put over adjacent Iba-1 stained sections. Within these ROIs (i.e. demyelinated grey matter or NAGM), 10 microglia with a visible Iba-1+ cell body and haematoxylin-stained nucleus were randomly selected throughout and traced. Microglia were traced using a 63× oil objective.Iba-1Microglia tracings were analysed using the Neuronal morphology analysis package (NeuroM) for Python. Using this toolkit, the total length of all processes per cell and the total amount of forking points (i.e. a split in the process) per cell were quantified.lme to run linear mixed models and emmeans for post hoc analysis. Similar to the RNA-seq analysis, ‘tissue area’  was entered as a fixed factor and ‘patient’ as a random variable. To facilitate comparisons to RNA-seq data, we report the false discovery rate (FDR). Semi-automatic quantification of immunoreactivity was analysed using repeated-measures ANOVA followed by the Tukey post hoc test. Analysis of microglia morphology data was assessed using linear mixed models with the patient as a random factor and lesion type area as fixed factors.Statistical analysis of all qPCR data was conducted using R using the packages https://www.ncbi.nlm.nih.gov/geo/ under the accession number: GSE149326.RNA-sequencing data presented in this study have been deposited at + cells; however, in order to facilitate the interpretation of the subsequent RNA-sequencing results from these lesions, we scored the white matter demyelinated area of the leucocortical lesion based on MHC-II+ as is the convention for white matter lesions (WMLs).30 Based on the presence of MHC-II+ cells observed in a rim surrounding the hypocellular white matter demyelinated area , half of the lesion was frozen and the other half fixed in formalin and paraffin embedded. When possible, lesion characterization was later verified using these FFPE tissue blocks. See ted area  and in tted area , the pre0+ cells , we scorg matter . Additio0+ cells .PLP1, MAG) and oligodendrocyte-related genes  which were highest expressed in the NAWM followed by the NAGM and as expected showed low expression in demyelinated white and grey matter. We also observed a clear separation in the expression of neuronal-related genes  microglial/macrophage  and immune responses . An overview of all genes found to be differentially expressed between the four areas can be found in RBFOX3)], demyelinated from normal-appearing (PLP1) and immune activation markers (HLA-DRA and GFAP) using RNA from a repeated experiment plus two additional RNA samples  = 14.98; P < 0.01, P = 0.077). In addition, we confirm that RBFOX3 mRNA is significantly lower in white matter areas compared with NAGM . Interestingly, RBFOX3 was also lower in demyelinated compared with NAWM  = 4.45; GML versus WML, P < 0.05; GFAP mRNA levels were higher in demyelinated white matter than NAWM  = 34.04; NAGM/GML versus WML, P < 0.0001; To verify that LCM dissected tissue from different regions as input for RNA sequencing yielded representative data, we used principal component analysis (PCA). We observed that white and grey matter samples were segregated in the first principal component (PC1) . In addi samples . These dITGAX) and SALL1 known to be expressed by microglia (32 Cluster 2 and Cluster 3 consisted of genes that were higher expressed in grey matter areas than white matter areas. These genes represent neuron (synapse)-related genes such as SCN1A33 and NRXN134 for Cluster 2 and OLFM1 and SHANK136 for Cluster 3  nuclei and thus there is likely an underestimation of the regulated OPC-related genes in our CIBERSORTx data. Compared with the NAWM, demyelinated white matter showed an increase in astrocyte and vasculature-related genes. This was not clearly observed in demyelinated grey matter compared with NAGM. An increase in vasculature-related genes is likely explained by an altered blood–brain barrier function in demyelinated white matter where infiltration of leucocytes is present. In line with this, we observed regulation of genes such as ICAM2, CLDN5, JAM2 and CDH5, all involved in the regulation of the blood–brain barrier.37–39 In addition, demyelinated white matter showed a surprising increase in excitatory neuron-related genes which was not found in demyelinated grey matter (Our results indicate that (demyelinated) white and grey matter have distinct gene expression profiles which may be partly due to different cellular compositions. To assess the cellular composition of the four areas represented in this study, we utilized CIBERSORTxy matter . Of notey matter . We obsey matter . Strikiny matter , highligITGAX, a representative gene of Cluster 1. ITGAX mRNA levels were indeed higher in demyelinated grey matter , but not in demyelinated compared with NAWM  = 17.04; P < 0.001, +/Iba-1+ cells in demyelinated grey matter compared with NAGM was observed. Representative images of CD11c immunoreactivity are presented in 19 we assessed whether CD11c+ cells have phagocytic properties. Indeed, we found that both amoeboid- (as observed in demyelinated white matter) and ramified-shaped CD11c+ microglia (as observed in demyelinated grey matter) can also express the lysosomal marker CD68, indicating lysosomal activity. We observed that microglia in demyelinated grey matter, even though they did not feature an amoeboid morphology, like microglia in demyelinated white matter, did show decreased morphological complexity compared with microglia in the NAGM as indicated by lower total length of the microglial processes  was significant in demyelinated white matter compared with NAGM (P < 0.05) and demyelinated white matter compared with demyelinated grey matter  = 5.61] in demyelinated white matter compared with normal appearing (P < 0.05) and demyelinated grey matter (P < 0.05) but not NAWM  and vesicular glutamate transporter 1 (VGLUT1) (gene name SLC17A7), both representative neuron/synapse-related genes in Cluster 2 (40 VGLUT1 immunoreactivity was (similar to gene expression levels) high in grey matter areas, showing no difference between demyelinated and NAGM  gaining insight into multiple sclerosis lesion formation in the white matter. However, it has long been known from histopathological studies that demyelinated grey matter does not feature many pathological events present in the demyelinated white matter such as infiltration of leucocytes or glial activation.3 To gain more insights in the pathology of demyelinated white and grey matter, we present gene expression of demyelinated and NAWM and NAGM areas of leucocortical multiple sclerosis lesions micro-dissected from post-mortem human brains. The strength of this study is that the experimental set-up, allowed for a direct comparison of gene expression within and between tissues taken from different patients. Generating these data was only possible by adopting a strategy where we utilized LCM to micro-dissect histologically confirmed (demyelinated) white and grey matter areas. As the micro-dissection procedure could affect the quality of the RNA used for sequencing, affecting the results, we repeated the experiment  by qPCR analysis as an external validation of the data. Moreover, immunohistochemical analysis was performed on FFPE mirror blocks as well as tissue from additional patients to validate certain gene data from our RNA-seq analysis at the protein level.In recent years, there has been a variety of studies published reporting gene expression of (laser-captured) demyelinated and normal-appearing human white matter , shown by us and others to be expressed in the brain, do show increased expression in the demyelinated grey matter.31ITGAX has been shown to be upregulated in the white matter during de- but also remyelination19 and has been associated with phagocytic disease-associated microglia observed in, for example, Alzheimer's disease.42 Another gene representative for Cluster 1 is increased expression of SALL1, a transcriptional regulator of microglia identity.32 Interestingly, downregulation not upregulation of SALL1 has been shown to lead to a pro-inflammatory microglial signature.32 Thus indicating that in the absence of infiltrated leucocytes (as is the case in the demyelinated grey matter), microglia in the demyelinated grey matter show a different inflammatory phenotype than in demyelinated white matter. Possibly, the increase in CD11c and CD68 in the demyelinated grey matter could indicate increased phagocytic activity and clearing of debris in these lesions, which could pave the way to more efficient remyelination and decreased inflammation which could save neurons from degeneration.43Analysis of the cluster of genes highest expressed in demyelinated grey matter indicated increased gene expression of genes involved in immune response and cellular activation. As previously mentioned, data from pathological studies have shown that a distinct pro-inflammatory response as visible in the demyelinated white matter is not present in the demyelinated grey matter. Yet, genes representative for Cluster 1 include 15 This could indicate that depending on the pathology present  or the type of lesion studied, i.e. subpial lesions15 or leucocortical lesions (this study), the microglial phenotype can be different. It would be of interest for future work to compare the microglial response in GML subtypes.Studying microglia morphology in normal-appearing and demyelinated grey matter, we observed that microglia had a more reactive phenotype, as indicated by less length and complexity of their ramifications in demyelinated compared with control grey matter. This is in contrast to what is previously reported in the cortex of multiple sclerosis patients with meningeal inflammation, where a microglia phenotype with increased complexity is observed.et al.44 who also reported lack of changes in synaptic-related genes in cortical areas of multiple sclerosis patients. Though we did not quantify the immunohistochemical signal, we observed increased expression at the protein level for EAAT2 (encoded by the gene SCL1A7) and VGLUT1 (SCL17A7) in the demyelinated white matter. Expression of EAAT2 in neurons/neurites has been shown before and could possibly be indicative of a specific subtype of EAAT2 which is expressed by neurons instead of astrocytes.46 The morphology of EAAT2+ structures in the demyelinated white matter is representative of dystrophic neurites indicating neurite damage.40 In addition, increased density of VGLUT1+ structures likely indicate increased glial/axonal contact, possibly to support axonal function or in an effort to modulate glutamate excitotoxicity.48 Thus the altered immunoreactivity of VGLUT1/EAAT2 in the demyelinated white matter could indicate neurite transection and could explain the increase in excitatory (glutaminergic) neuronal-related genes visible in the CIBERSORTx analysis which is not prominent in the demyelinated grey matter. Of possible interest is that the different microglial inflammatory phenotype observed in the demyelinated grey matter may prevent local neurite damage. In support of this option, we found that C1QB, related to neuronal damage, is expressed lower in demyelinated grey matter than in the NAGM, possibly indicating some protective effect. In contrast, we observed increased gene expression of C1QB, which is related to lower VGLUT1 expression49 and neuronal/neurite damage in the demyelinated white matter only. Interestingly, in a recent study using single-cell sequencing of multiple sclerosis WML material, C1QB has been identified as a marker for ‘microglia inflamed in multiple sclerosis’.50 The increased expression of C1QB in demyelinated white matter and lowered expression in demyelinated grey matter observed in this study may imply that the classical inflamed microglial signature may not be present in the demyelinated grey matter.We also observed two clusters of genes which were higher expressed in the demyelinated white matter and were lower expressed or showed no change in expression compared to normal-appearing matter and the demyelinated grey matter (Cluster 2 and Cluster 6). Cluster 2 is characterized by neuronal-related genes. Similar to gene expression results, overall, we observed very little differences in EAAT2 or VGLUT1 immunoreactivity in demyelinated compared with NAGM indicating that neurite damage in these areas may be less prominent. This is in agreement with data by Dutta STAT3. Expression of STAT3 is shown to be upregulated in infiltrated myeloid cells near the active demyelinating white matter.51 Decreased expression of STAT3 by infiltrated myeloid cells impaired antigen presentation to CD4+ T cells, thereby hindering differentiation of CD4+ T cells to T-helper cells.51 Thus, increased expression of STAT3 in demyelinated white matter could be indicative of a role for infiltrated myeloid cells, which are absent in the demyelinated grey matter which indeed shows lower expression of STAT3. Genes represented in another cluster of genes higher expressed in the demyelinated white matter (Cluster 5), PLSCR4 and JUN, also implicate astrocyte–CD4+ T cells interaction in demyelinated white matter only. We observed that PLSCR4, a phospholipid scramblase,52 expressed by astrocytes, is selectively upregulated in the demyelinated white matter at both the gene and protein level. PLSCR4 is known to interact with CD4+ T cells52 In addition, increased gene expression of JUN, which has been described to be expressed by a cluster of astrocytes propagating disease which is driven by MAF BZIP Transcription Factor G (MAFG) which in turn is induced by GM-CSF released by T cells, also suggest astrocyte T-cell interactions in the demyelinated white matter only.21 It is postulated that the astrocytic reactivity as defined by a hypertrophic morphology could be driven by the interaction of astrocytes with T cells as it has been shown that astrocytes extend their processes towards T cells in vitro.53 This increase in astrocyte reactivity could lead to increased extracellular matrix deposition,11 as is indicated by the pathway analysis, which could modify the lesion environment and hinderj remyelination.12Cluster 6, defined by genes higher expressed in the demyelinated white matter and lower expressed in the demyelinated grey matter also includes the transcriptional activator 21 notably absent in the demyelinated grey matter. In addition, in more inflammatory demyelinated white matter, we observed clear neurite damage, which we did not observe in the neighbouring grey matter. This could implicate that neurite damage (as observed by increased EAAT2) and subsequent alterations in excitatory synapses are mediated by inflammation through activated microglia as proposed before54 and therefore not present in the demyelinated grey matter samples used for this study which featured possibly a different type of inflammatory microglia. Though our data indicate neuroprotective events present in demyelinated grey matter of leucocortical lesions, grey matter demyelination or neuronal degradation may ultimately be responsible for the silent disease progression observed in long-term multiple sclerosis patients.56 Moreover, multiple sclerosis patients that present with a cortical first phenotype  are less likely to respond to treatment,56 possibly as grey matter pathology is less associated with overt classical inflammation. Further study of pathological and cellular events in the (demyelinated) grey matter could provide more insights in multiple sclerosis disease pathology. For example, in this study, we focused on demyelinated grey matter of leucocortical areas whereas much of the current literature focuses more on subpial demyelination . More research is needed into comparing pathological events in different GMLs as these may have different origins.17 In this respect, we propose that targeting white or grey matter-specific multiple sclerosis pathology-associated genes could possibly be more beneficial for the treatment of progressive multiple sclerosis when grey matter demyelination becomes more prominent.Taken together, our study highlights distinct gene expression profiles of demyelinated white and grey matter areas in multiple sclerosis, within the same leucocortical lesion. The here observed limited overlap in gene expression between demyelinated white and grey matter can have potential consequences for the understanding of local ongoing pathological processes and identifying subsequent targets for the development of treatment for multiple sclerosis. Currently, treatment development and efficacy are mostly based on inflammatory demyelinated white matter lesions, as these are readily visible on conventional MRI, whereas demyelinated grey matter is not. Though we find evidence for inflammatory activity in both demyelinated white and grey matter, the inflammatory profile in these two lesion types is very different. Microglia in the demyelinated grey matter may adopt a more anti-inflammatory signature after demyelination in the absence of infiltrated leucocytes, whereas in the demyelinated white matter, inflammation may be propagated by astrocyte T-cell interactionsfcac005_Supplementary_DataClick here for additional data file.