Appropriate probes for RNA previously focused on single fluorine atoms attached to the 5-position of pyrimidine nucleobases or at the ribose 2′-position. To increase NMR sensitivity, trifluoromethyl labeling approaches have been developed, with the ribose 2′-SCF3 modification being the most prominent one. A major drawback of the 2′-SCF3 group, however, is its strong impact on RNA base pairing stability. Interestingly, RNA containing the structurally related 2′-OCF3 modification has not yet been reported. Therefore, we set out to overcome the synthetic challenges toward 2′-OCF3 labeled RNA and to investigate the impact of this modification. We present the syntheses of 2′-OCF3 adenosine and cytidine phosphoramidites and their incorporation into oligoribonucleotides by solid-phase synthesis. Importantly, it turns out that the 2′-OCF3 group has only a slight destabilizing effect when located in double helical regions which is consistent with the preferential C3′-endo conformation of the 2′-OCF3 ribose as reflected in the 3J (H1′–H2′) coupling constants. Furthermore, we demonstrate the exceptionally high sensitivity of the new label in 19F-NMR analysis of RNA structure equilibria and of RNA–small molecule interactions. The study is complemented by a crystal structure at 0.9 Å resolution of a 27 nt hairpin RNA containing a single 2′-OCF3 group that well integrates into the minor groove. The new label carries high potential to outcompete currently applied fluorine labels for nucleic acid NMR spectroscopy because of its significantly advanced performance.New RNA modifications are needed to advance our toolbox for targeted manipulation of RNA. In particular, the development of high-performance reporter groups facilitating spectroscopic analysis of RNA structure and dynamics, and of RNA–ligand interactions has attracted considerable interest. To this end, fluorine labeling in conjunction with  3 label for nucleic acid NMR spectroscopy carries high potential to outcompete currently applied fluorine labels because of significantly advanced performance.The new 2′-OCF To the best of our knowledge, only one study is available that describes the thermodynamic stabilities of a short DNA containing a single 2′-OCF3 group that is paired to either a complementary DNA or RNA strand.31 We expected the 2′-OCF3 modification highly beneficial for RNA and 19F-NMR spectroscopic applications to analyze structural dynamics and ligand interactions, and therefore, we set out to overcome the underlying challenges in chemical synthesis. In this work, we present synthetic routes toward 2′-OCF3 nucleoside phosphoramidites and their incorporation into oligoribonucleotides by RNA solid-phase synthesis. Furthermore, we describe the impact of the 2′-OCF3 group on thermodynamic stability of RNA double helices which is only slightly destabilizing when located in double helical regions. This finding is consistent with a preferential C3′-endo conformation of the 2′-OCF3 ribose in short single stranded RNA as shown by measurements and interpretation of H1′–H2′ coupling constants. Moreover, we demonstrate the exceptionally high sensitivity of the new label in 19F-NMR analysis of RNA structure equilibria and of RNA–small molecule interactions. The study is complemented by crystal structures of an RNA hairpin including 2′-OCF3 modifications.The attractiveness of fluorine labeling of biomolecules for 3 nucleoside phosphoramidites followed the previously described route of a 2′-OCF3 adenosine derivative31 and involved a method by Hiyama et al. who reported that methyl xanthates R-OC(S)SMe are converted into trifluoromethyl ethers R-OCF3 by treatment with pyridinium poly(hydrogen fluoride) (HF/pyridine) in the presence of 1,3-di-bromo-5,5-dimethylhydantoin (DBH).32 A drawback however is the low yield of this transformation.The syntheses of 2′-OCFC7  group -thiocarbonyl]cytidine derivative C3. After acetylation of the exocyclic NH2 group to furnish C4a, the desired 2′-O-trifluoromethyl derivative C5a was obtained in low yields by treatment with pyridinium poly(hydrogen fluoride) (HF/pyridine) in the presence of N-bromosuccinimide (NBS) instead of DBH as mentioned above. Finally, C5a was transformed into the dimethoxytritylated compound C6a, and conversion into the corresponding phosphoramidite C7a was accomplished in good yields by reaction with 2-cyanoethyl N,N-diisopropylchlorophosphoramidite. Starting with cytidine C1, our route provides C7a in 4% overall yield in six steps with six chromatographic purifications; in total, 0.5 g of C7a was obtained in the course of this study. We mention that the yields of trifluoromethylation can be increased by switching from N4-acetyl to N4-benzoyl protection (4b) against hydrolysis increased the yields by 50% comparing the transformation of 4a into 5a with 4b into 5b. However, one has to be aware that N4-benzoyl cytidine in synthetic RNA can be transaminated to some extent (<10%) when deprotection reagents containing methylamine are applied.33,34For building block C7 , we starS) group . Compounotection . The higA7 -thiocarbonyl]cytidine derivative A3. After benzoylation of the exocyclic NH2 group to furnish A4, the desired 2′-O-trifluoromethyl derivative A5 was obtained by treatment with HF/pyridine in the presence of NBS. Yields were significantly higher compared to the same transformation on cytidine (C4 into C5). Finally, A5 was transformed into the dimethoxytritylated compound A6, and conversion into the corresponding phosphoramidite A7 was accomplished in good yields by reaction with 2-cyanoethyl N,N-diisopropylchlorophosphoramidite. Starting with adenosine A1, our route provides A7 in 12% overall yield in six steps with six chromatographic purifications; in total, 1.2 g of A7 was obtained in the course of this study.For building block A7 , we starDS group . CompounO trifluoromethylation step, we tested silver-mediated oxidative O-trifluoromethylation of a 3′,5′-O protected adenosine derivative using TMSCF3, following a published protocol,65 but unfortunately failed. Currently, we are planning to elaborate routes for 2′-OCF3 uridine and guanosine building blocks via the here described methyl xanthate intermediates. This should be feasible provided proper protection concepts for N3-H and N1-H, respectively, can be identified.We mention that in an attempt to increase the yields for the 2′-3 modifications was performed following the 2′-O-[(triisopropylsilyl)oxy]methyl (TOM) approach.35,36 Coupling yields of the novel building blocks were higher than 98% according to the trityl assay. Cleavage of the oligonucleotides from the solid support and their deprotection were performed using methylamine in water/ethanol or methylamine/ammonia in water (AMA), followed by treatment with tetra-n-butylammonium fluoride (TBAF) in tetrahydrofuran. Salts were removed by size-exclusion chromatography, and RNAs were purified by anion-exchange chromatography under denaturating conditions GG-GCAA-CCUUCG , compared to the unmodified counterpart (Tm 73.3 °C). We remind that the same RNA but with a 2′-SCF3 modification at the same nucleotide position caused a much stronger drop in the Tm value, namely by 14 °C CC , compared to the unmodified counterpart.A single 2′-OCFg region . For insA-CCUUCG  revealedby 14 °C .29 As a CF3-A)CC  also suf3 at cytidine instead of adenosine, 5′-GAAGG-GCAA-C(2′-OCF3-C)UUCG UUCG . The des hairpin .29 We poc origin .3 group resides in a single-stranded region, the impact on thermodynamic stability is minor, and in the case of the extra-stable GNRA loop motif even modestly stabilizing, reflected by an increase of the Tm value and a favorable ΔG value for 5′-GAAGG-GC(2′-OCF3-A)A-CCUUCG GGC, and determined the 3J (H1′–H2′) coupling constant from a 2D 1H,1H double quantum filtered COSY spectrum (DQFCOSY). The coupling constant amounted to 5.9 Hz, which translates into a population of about 60% of C2′-endo ribose conformation in the single strand  coupling constants amount to 9.7 and 10.4 Hz,29,30 respectively, provided they reside in a single-stranded short RNA. This in turn stands for a population of almost 100% of C2′-endo conformation. The strong C2′-endo preference of 2′-SCF3 modified nucleosides likely provides a rationale for the significantly higher thermodynamic destabilization if they are encountered in base paired regions.29,30 Further support for this hypothesis stems from the analysis of 5′-GGCAG(2′-F-A)GGC. For the 2′-F labeled nucleoside, the 3J (H1′–H2′) coupling constant was less than 1 Hz  region. This sequence is known to be a robust and well-behaved crystallization scaffold that can accommodate small modifications.46,47 For the installation of 2′-OCF3, we deemed nucleotide A2670 appropriate which forms a Watson−Crick base pair with U2650 in the regular A-form double helical region of this RNA, as well as nucleotide C2667 which forms a water-mediated mismatch with U2653. The latter RNA crystallized in the same tetragonal crystal form as the unmodified RNA46 and also diffracted to atomic resolution (0.9 Å). A new monoclinic crystal form was obtained with 2′-OCF3 A2670-modified SRL RNA , containing three molecules per asymmetric unit and diffracting to 2.4 Å resolution , including sugar puckers of modified positions . It is tempting to assume that the short distance observed (2.9 Å) is indicative of a halogen bond, however, fluorine  usually retains a strongly electronegative electrostatic potential in biomolecules.63,64 Nevertheless, we note that the trifluoromethyl modification additionally comes also close the O4′ of the next G2668 residue . Likely, the rotation of the 2′-OCF3 group in the base-paired stem is sterically hindered leading to signal broadening due to exchange on the μs to ms time scale.ive fold . At roomobtained  and assiobtained . Interes3 label was also used to analyze the underlying kinetics of refolding for the bistable RNA. For that purpose, we used 19F longitudinal exchange NMR spectroscopy.53–55 As expected we found two 19F correlation peaks (fold A and fold B) in the 19F NOESY exchange spectrum of the RNA (with a single 2′-OCF3 labeled adenosine) for which exchange peaks could be identified . Using our 19F labeling approach, we were thus able to characterize the refolding kinetics of the bistable RNA under near physiological conditions.The sensitive 2′-OCFentified . The forentified . We foun3 at C10. As expected, we observed two distinct 19F NMR resonances for the two folds in slow exchange, albeit the chemical shift difference was smaller, consistent with the label located in double helical regions in both folds (3 A3 labeled counterpart (40 : 60) and confirmed the flexibility for 2′-OCF3 positioning despite of being a non-isosteric label. At this point, we also mention that the native (unmodified) RNA exists in a 50 : 50 equilibrium as was determined earlier.23Furthermore, to analyze the impact of the labeling position we synthesized the same bistable RNA but with 2′-OCFth folds . The equth folds  was only19F NMR resonances shifted to lower magnetic field. This shift was more pronounced for the 2′-OCF3–C labeled RNA  overnight. By adding the carbon dimension, better spectral dispersion is obtained, which in principle would allow to apply 13C ZZ exchange spectroscopy in 2′-O13CF3 labeled systems. It will be further highly interesting to explore the TROSY properties of the 2′-O13CF3 methyl group in RNA by combining 19F and 13C stable isotope labeling. In a recent work by Sykes and co-workers no favorable TROSY effect in proteins was found using 3-bromo-1,1,1-trifluoroacetone as labeling reagents. The absence of the TROSY effect was attributed to the dominating CSA relaxation mechanism in the alpha CF3 group suggesting that gains from CF3-HMQC experiments should be only observable at low magnetic fields.62 Nevertheless, it remains to be clarified if the 2′-O13CF3 shows a more beneficial TROSY effect due to the different chemical environment.Finally, we characterized both RNAs by recording  spectra . We were3–label is highly practical for NMR studies of RNA–ligand interactions. In 1) sensing class-I riboswitches from Thermoanaerobacter tengcongensis (Tte) (Fusobacterium nucleatum (Fn) is illustrated  ∼20 nM versus Kd(Fn) ∼260 nmol).57,58 Moreover, for the Tte riboswitch, crystal structures at high resolution exist of both, the free and the preQ1-bound RNA,57 and therefore a solid foundation to explore the underlying conformational adaptions during ligand-induced folding is available.The new 2′-OCFis (Tte)  and Fusoustrated .56–58 Th1 riboswitch is shown. The two RNAs with a single 2′-OCF3 label at either position 27 or 32 give a single 19F resonance in the absence of Mg2+  is added, the RNA becomes partially preorganized into a pseudoknot. Interestingly, the label at position 32 indicates heterogeneities within (or close to) the loop–tail interaction, reflected by the occurrence of three, partly overlapping 19F-resonances (15N-labeled) base pairs of the same riboswitch system was tracked.59 We furthermore believe that two of the three folding states likely correspond to the conformational distinction seen in high-resolution crystal structures of free versus preQ1-bound Tte RNA. In the ligand-free pseudoknot form, A14 takes the position of the preQ1 ligand and is stacked between the two base pairs of G11–C30 and G5–C16.57,59 This observation can be rationalized by a conformational rearrangement from a solvent-exposed base C15 to a flipped-in conformation of C15 that becomes the Watson–Crick pairing partner of preQ1 in the ligand-bound pseudoknot RNA.60At the left side of Fn preQ1 riboswitch which binds preQ1 13-fold weaker compared to the Tte counterpart. Furthermore, the loop size of the Fn RNA contains two additional nucleotides and the pseudoknot interaction allows formation of a continuous 4 bp double helix.61 We placed the 2′-OCF3 labels in different positions, one in the tail to sense pseudoknot formation, and the other one in the loop next to the cytosine that can form a Watson–Crick base pair with preQ1. Both labels allow to monitor the binding process. By increasing the concentration of preQ1 stepwise, an increasing fraction of the preQ1-RNA complex is observed, reflected in a second 19F resonance that is shifted to lower field. In contrast to the Tte RNA, the Fn riboswitch shows a significant population of unbound RNA, even at a ten-fold excess of ligand over RNA. Furthermore, a folding intermediate is also indicated by the appearance of a third resonance for the 2′-OCF3 A16 labeled RNA, although existing in a minor population only. Another interesting feature is that the line width of the 19F resonance of the label next to the pseudoknot double helix (A30) is rather small and the OCF3 group seems to be hardly hindered in rotational freedom compared to the other three labeling positions in the two riboswitch systems.19F labels for nucleic acids NMR spectroscopy have been introduced, among them are single fluorine labels , trifluoromethyl labels methyl] ribose,27 2′-SCF3,28–30 and very recently 8-CF3 guanine,66), and a nine-fluorine-atom label but-1-ynyl] 2′-deoxyuridine).4 Comparing them, the first subgroup is superior with respect to steric demands but suffers from low sensitivity and the need for proton decoupling;21–25 the opposite is true for the nine-fluorine-atom label which is highly sensitive but sterically demanding.4 Therefore, trifluoromethyl labels appear to be a good compromise.26–30 The pyrimidine 5-CF3 group, however, has been described to be chemically unstable during deprotection of synthetic nucleic acids, resulting in low yields and length limitation.26 Differently, the 2′-SCF3 group – although synthetically well accessible – strongly affects thermodynamic base pairing strength which is a drawback for applications that aim at the elucidation of folding pathways.28–30 All critical aspects and requirements are very well satisfied by the new 2′-OCF3 label as shown in this study; it therefore has potential to advance to the most broadly applicable fluorine label in NMR spectroscopy of nucleic acids. The 2′-OCF3 group possesses pronounced sensitivity and exhibits large chemical shift dispersion so that it is possible to distinguish distinct base sequences in double helical regions. Moreover, the ribose 2′ position guarantees principal synthetic accessibility to all of the four nucleosides. Additionally, the size of the OCF3 label matches the naturally occurring and widespread 2′-OCH3 group. For the investigation of naturally occurring 2′-OCH3 modification patterns in biologically meaningful settings, the 2′-OCF3 group will be an ideal candidate.Over the last decades, several There are no conflicts to declare.SC-011-D0SC04520A-s001


CD11b NCM, clinical covariates and neurofilament light-chain plasma concentration at the baseline predicted shorter survival in a multivariable and univariate analysis . Blood samples with the highest frequencies of active CD11b+ IM and NCM contained the lowest concentrations of soluble CD11b. Our preliminary data suggest that the levels of active CD11b+ monocytes and NCM in the blood predict different clinical outcomes in ALS.Monocytes expressing the inflammation suppressing active CD11b, a beta2 integrin, may regulate neuroinflammation and modify clinical outcomes in amyotrophic lateral sclerosis (ALS). In this single site, retrospective study, peripheral blood mononuclear cells from 38 individuals living with ALS and 20 non-neurological controls (NNC) were investigated using flow cytometry to study active CD11b integrin classical (CM), intermediate (IM) and non-classical (NCM) monocytes during ALS progression. Seventeen ALS participants were sampled at the baseline (V1) and at two additional time points (V2 and V3) for longitudinal analysis. Active CD11b+ CM frequencies increased steeply between the baseline and V3 (ANOVA repeated measurement,  Amyotrophic laterals sclerosis (ALS) is, in most cases, a rapidly progressing and invariably fatal neurodegenerative disorder. The extent of upper and lower motor neuron involvement and of limb and bulbar functions impairment at the onset and in the progression of the disease conditions the significant variability in survival, ranging from less than a year (fast progressing ALS—“A-F”) to more than 5 years from symptoms onset (slow progressing ALS—“A-S”) . MeasureThe progression of ALS has been linked to immunological dysregulation, including changes in the expression of cells of the myeloid progenitor lineage in circulation ,6,7,8. PMonocytes’ accumulation in tissues where inflammation is unravelling is modulated by beta2 integrins, including CD11b, whose activated form suppresses inflammation . In ALS,The demographic and clinical characteristics of the study participants are reported in R = −0.4342, p = 0.0065, R = 0.6567, p = 0.0031; Group analysis to compare the expression of monocyte subtypes at the baseline in ALS, NCM and ALS phenotypic variants did not show any significant difference. A pairwise correlation analysis (Spearman’s) showed a correlation between levels of CD11b+ NCM and ΔFRS , while high frequencies of baseline active CD11b+ NCM correlated with increased survival . These data suggest that the expression of NCM at the baseline is linked to a worse prognosis, while a better outcome is observed with high baseline frequencies of the same cells expressing active CD11B . These data indicated that higher baseline NCM had a negative effect, while higher baseline ALSFRS-R scores a positive effect on survival  , ΔFRS  and NfL  were predictors of shorter survival. Higher frequencies of CD11b+ NCM were also predictors of shorter survival .To study the prognostic utility of NCM, IM and CM monocyte subsets’ expression at the baseline, we fitted a linear model to estimate the effect on the disease progression rate from the onset to baseline (ΔFRS) of predefined predictor variables using multivariable linear regression analysis. To estimate the impact on survival, we utilized Cox regression in the same model . The clionstant) . In linep = 0.0078; p = 0.0195 and 69% reduction, p = 0.0023, respectively; p = 0.0022, p = 0.0109, data not shown).The ability to predict the survival to permanent assisted ventilation (PAV), tracheostomy-free survival or last follow-up of relevant monocyte subsets was also evaluated using Kaplan–Meier (univariate) analysis. Monocyte subsets, including active and non-active CD11b cells, were dichotomized in above median (or upper tertile) and below median (or lower tertile) frequencies. In line with previous reports ,24, highp < 0.0001). Of all the biological markers under investigation, only the NfL blood concentrations at the baseline could discriminate patients with ALS from NNC using a receiver operating characteristic binary classification system , of CD11b+ NCM  and of CD11b+ IM  were observed with increasing age at the baseline  were associated with younger age. Using our linear regression model to examine the effect on the expression of each of the monocyte subsets under investigation, we also showed that age at baseline independently affected the expression of active CD11b+ NCM , as well as of NCM  and of CD11b+ IM . The frequencies of the same monocyte subsets did not show any correlation with age at the baseline in NNC (data not shown).Higher frequencies of HLA-DR+  A and in p = 0.0011 and p < 0.0001, respectively) in contrast with the stability across time points of CD11b+CM (data not shown).An ANOVA for repeated measurements in a mixed model confirmed the strong up-regulation of active CD11b+ CM in V2 and V3 compared to the baseline in ALS and in A-S  only in the HC and ALS samples combined. In NNC, the sCD11b plasma concentrations did not correlate with any of the CD11b or active CD11b-expressing monocyte subset frequencies (data not shown).We looked at the correlation between soluble CD11b (sCD11b) plasma concentration and the frequency of each blood monocyte subset expressing CD11b or activated CD11b in all the samples and separately in the ALS and NNC samples. Four individuals (two ALS and two NNC) had a much higher sCD11b plasma concentration compared to the other participants (>400 ng/mL) and, therefore, correlation analyses were performed with and without these outliers. Active CD11b IM and CM monocyte subsets frequencies showed an inverse correlation with sCD11b blood concentrations both in ALS  and monocyte subsets, including those with an active and non-active CD11b phenotype. Integrins such as CD11b are normally positioned on cell membranes, and their release into the extra-cellular space may result from alternative mRNA splicing, which produces polypeptides lacking a transmembrane region. Their transition into extracellular fluids may also be caused by a proteolytic cleavage from the cell surface. Soluble integrins have been reported to modulate the interaction between ligands and membrane-bound integrins affecting cell homeostasis in different pathological processes . In the Our results suggest that the appearance of specific monocyte subsets in the peripheral circulation may be a prognostic indicator in a clinically heterogeneous disorder such as ALS. It also provides an indication that integrins may have a role as immunoregulatory factors that could be exploited as novel biomarkers and therapeutics for ALS. A limitation of our study is the partial understanding of the longitudinal expression of active CD11b + monocytes, given the relatively small and uneven longitudinal cohort employed in our study. Further investigations on a larger and more homogeneous longitudinal ALS population will be required to better characterize the behavior of monocytes and define their contribution to the inflammatory response that accompanies motor cell loss in the progression of ALS. It will also be important to use an unbiased approach to profile monocyte cell lineages and obtain further insight into the molecular composition of each cell type at a protein and RNA level and in different stages of the disease.The blood samples used for the analysis were collected from individuals enrolled in the ALS biomarkers study (REC reference 09/H0703/27). We conducted a retrospective, single-centre study where participants and their blood samples were selected from individuals enrolled in the ALS biomarkers study between 06/2017 and 03/2020. These included ALS patients diagnosed according to the El Escorial Criteria (n:38) and non-neurological controls . ALS patients and NNC had comparable gender distribution and age at the time of baseline sampling. Exclusion criteria for ALS and NNC were systemic or organ-specific autoimmune disorders, recent treatment with steroids, immunosuppressants or immunoglobulins and recent injuries. For longitudinal analysis, additional samples were collected from a subset of ALS individuals (n:17) at two separate time points from baseline (V2 and V3). Time intervals from baseline to V1 and V3 are reported in Plasma was separated from heparinized blood and the remaining cell component diluted with an equal amount of Dulbecco’s Phosphate-Buffered Saline. PBMC were isolated, stratifying 30 mL of diluted blood on 15 mL of lymphoprep-TM (Density gradient medium), followed by centrifugation at 2000 rpm for 40 min at room temperature (RT). PBMC’s cryopreservation for long-term storage was in 10% dimethyl sulfoxide freezing solution and foetal bovine serum. Plasma extraction was performed by centrifugation at 3500 rpm for 10 min at RT and stored at −80 °C.6 cells per tube) were stained with 100 μL of diluted viability dye zombie aqua  for 15 min and 50 μL diluted FcBlock (Human BD) was added. Unconjugated primary antibodies (CD11b (ICRF44)-BV421, CD14 (M5E2)-BV605, HLA-DR (G46-6)-BV650, CD16 (3G8)-PE-CF594, active CD11b (CBRM1/5)-PE-Cy7) were incubated at RT for 30 min. 50 μL of brilliant stain buffer was added to reduce staining artefacts. Cells were analyzed using the NovoCyte® 13-colour flow cytometer configured with 405, 488 and 640 nm lasers. The NovoExpress® software was used for data acquisition and analysis. Dead cells were excluded by forward-side scatter gating and monocyte sub-classes gated using CD14/CD16 and then CD11b or active CD11b  using a digital immunoassay HD-1 Analyzer , while soluble CD11b plasma measurement was performed by ELISA using a commercial immunoassay (ELH-ITGAM—RayBio). ALS and HC plasma samples were equally distributed on each plate and measured in duplicates.Kruskal–Wallis nonparametric test was used to compare biomarker levels across ALS patients’ subgroups and NNC. To investigate the stability of monocyte expression levels over time, we used the z-score of the means of V2/baseline ratios (log-2 transformed) to represent any increase or decrease over time (x axis) and the standard error (SE) of the same log 2 V2/V1 fold changes (Y axis) to represent inter-individual variability . For longitudinal analyses, ANOVA for repeated measures fitting a mixed model with missing values at random was applied along with spaghetti plot representation. Pairwise correlation analysis was applied to test the association between relative percentages of monocyte subsets, functional scores and age. Survival was calculated as the time from baseline (V1) to permanent assisted ventilation (PAV—≥22 h/day), tracheostomy or death. Progression rate was obtained subtracting the ALSFRS-R at baseline from 48  divided by the time in months from reported first symptoms of disease to baseline  or subtracting the last recorded ALSFRS-R score from 48 and dividing by the interval between visits in months . Individuals with ALS and a slower progression of the disease had a ΔFRS and ALSFRS-R change higher than - 0.5 points/month (A-S) while those with a faster disease progression had ΔFRS and ALSFRS-R smaller than −1.A linear model was fitted with pre-defined variables already reported as predictors of the rate of disease progression and survival in ALS. Explanatory (independent) variables included gender, age at baseline (V1), bulbar onset of the disease, baseline ALSFRS-R, NfL and monocyte subset frequencies, while ΔFRS was the dependent variable. The prognostic value of these pre-defined variables and of the circulating monocytes subsets were also evaluated using Cox regression analysis, with permanent assisted ventilation (PAV) and tracheostomy-free survival from baseline as outcome. Frequencies of monocyte sub-classes were incorporated as continuous variables in the Cox prognostic and linear regression model. The ability to predict survival of relevant monocyte subsets was also evaluated using a univariate Kaplan–Meier analysis with survival from baseline. Monocyte subsets, including active and non-active CD11b cells, were dichotomized in above median or upper tertile and below median or lower tertile frequencies. Receiver operating characteristic (ROC) curve non-parametric analysis was used to discriminate between phenotypic variants and between ALS and HC based on plasma NfL concentrations. Continuous variables were expressed as median  or mean . Monocyte sub-class frequencies were expressed as percentage of parent cells. Monocyte markers expression values were not normally distributed; hence, natural logarithm transformation was used in the analyses. Data were analysed using Prism  and SPSS.


A major part of the transcriptome complexity is attributed to multiple types of DNA or RNA fusion events, which take place within a gene such as alternative splicing or between different genes such as DNA rearrangement and trans-splicing. In the present study, using the RNA deep sequencing data, we systematically survey a type of non-canonical fusions between the RNA transcripts from the two opposite DNA strands. We name the products of such fusion events cross-strand chimeric RNA (cscRNA). Hundreds to thousands of cscRNAs can be found in human normal tissues, primary cells, and cancerous cells, and in other species as well. Although cscRNAs exhibit strong tissue-specificity, our analysis identifies thousands of recurrent cscRNAs found in multiple different samples. cscRNAs are mostly originated from convergent transcriptions of the annotated genes and their anti-sense DNA. The machinery of cscRNA biogenesis is unclear, but the cross-strand junction events show some features related to RNA splicing. The present study is a comprehensive survey of the non-canonical cross-strand RNA junction events, a resource for further characterization of the originations and functions of the cscRNAs. Gene fusion, trans-splicing or transcription read-through contributes to generation of chimeric RNA. Here the authors develop a pipeline to identify non-canonical type of chimeric RNAs called cross-strand chimeric RNA (cscRNA), which are fused between two precursor RNAs transcribed from the opposite DNA strands. Variations in RNA splicing, which result in different isoforms of the mature RNA products from the same gene, is a major machinery contributing to the transcriptome complexity in the species of metazoans4. In addition, gene fusion generates hybrid RNA products, i.e., chimeric RNAs, and brings the transcriptome complexity to a higher level7.It has been increasingly recognized that the genetic and epigenetic information is subjected to complicated processing during and after transcription, which gives rise to the extensive transcriptome complexity8. Gene fusion due to chromosome rearrangement is a well-characterized hallmark of cancer9, which is infrequent in the non-cancerous contexts10. Many of such gene fusion events give rise to fusion proteins that often play critical roles in cancer development, maintenance, and progression11. Therefore, several fusion proteins, for example, BCR-ABL1 in chronic myelogenous leukemia12 and EML4-ALK in lung cancer13, serve as effective therapeutic targets15. Another type of gene fusion takes place between RNA molecules in the absence of DNA rearrangement, for example, trans-splicing or transcription read-through between two precursor RNAs16. Such non-canonical RNA splicing between different genes is another source of transcriptome complexity17, which has been frequently reported in both cancerous and normal cell contexts23. Examples of such RNA chimeras include JAZF1-SUZ12 (JAZF1-JJAZ1)10, PAX3-FOXO121, and SLC45A3-ELK424, which were shown to be physiologically relevant and subjected to sophisticated regulations on their prevalence.Chimeric RNAs could be results of chromosome rearrangement at the DNA level or non-canonical RNA splicing at the RNA level27. In many cases, such geometric closeness was mediated by base-pairing between the complementary sequences of the precursor RNAs29. Here, we reason that by definition, the overlapping bi-directional transcripts from the two opposite strands of the genome DNA within a limited region should share complementary sequences and should be geometrically close before leaving their transcription sites. Therefore, we ask whether these transcripts could form a different type of chimeric RNAs, which are cross-strand RNA fusion products, i.e., cross-strand chimeric RNAs (cscRNAs).In general, the trans-splicing that generates chimeric RNA and the back-splicing that produces circular RNA depend on spatial proximity between the RNA fragments to be fused together34. Their main aims are mostly identifications of the gene fusion events between the annotated transcripts. Therefore, although thousands of chimeric RNA species have been identified37, including small numbers of cscRNAs formed between previously known transcripts, none of the current methods can perform de novo identification of the cscRNAs in an unbiased and comprehensive manner. In the published databases for the gene fusion events, the category of cscRNA has been largely overlooked, except for ChiTaRS, which, to our best knowledge, is the only database hosting an archive of the cross-strand fusion transcripts36. In fact, the chimeric RNA species in ChiTaRS were identified from the ESTs and mRNAs from GenBank. These sequences from different studies were all pooled together, and the context information is lost. Here we designed a specialized bioinformatics pipeline cscMap for de novo identification of the cross-strand chimeric RNAs directly from the RNA-seq reads, in a context-specific manner. A series of measurements were carefully implemented by cscMap to obtain high accuracy for capturing the cross-strand junction events. We used the RNA deep sequencing data from a large variety of biological samples in the ENCODE consortium and the GEO database. Interestingly, large numbers of cscRNAs were identified in both the normal and cancerous contexts, and they were found in multiple species as well. Experimental validations and other features of these chimeric RNAs suggested that most of them are not products of genome rearrangement and that they are unlikely to be just technical artifacts or biological noise. These cross-strand RNA fusion events predominantly take places between products of the bi-directional convergent transcriptions. Further analyses then suggested that these cross-strand RNA junctions are potentially related to RNA splicing and possibly other transcriptional and post-transcriptional processes. Although the detailed machinery of cscRNA biogenesis and their potential functions are still unclear, we propose that they represent an additional level of the transcriptome complexity, which is worth further investigation. The present study hereby provides a comprehensive survey of the cscRNAs in a large variety of samples, which should serve as an insightful resource to inspire and support further elucidation of the biological relevance of the cscRNAs.Several bioinformatics pipelines have been developed to search for the chimeric RNA transcripts with RNA sequencing dataWe developed a bioinformatics pipeline cscMap to search for the cscRNAs, i.e., RNA chimeras resulted from fusions of the transcripts encoded by the two opposite DNA strands Fig. . While mThe pipeline was first applied on 271 datasets of total RNA PE sequencing generated by ENCODE, which offers advantages of high sequencing depths, large sample size, tissue type variety, and strand-specific sequencing with long paired-end reads. These are all human samples including 35 normal tissues, 52 primary cells, and 27 cancer cell lines, and each type of the samples has at least two biological replicates  and 735 (for the 3′) of them were also found in the mouse cscRNA library.Furthermore, cscMap has also identified large numbers of cscRNAs using many other RNA-seq datasets from the GEO database Fig. , which woli Fig. , indicatoli Fig.  were als42. A very small proportion  of the 5′ and 3′ junction sites were indeed consistent with the DNA recombination breakpoints. In addition, we used the DNA-seq data of 9 breast cancer cell lines (GSE48216)44, and only 0.6% (320/53688) of the cscRNAs or 0.9% of the recurrent ones were supported by the DNA-seq data  around the junction sites. In addition, the sequencing read-mapping errors could also give rise to false discoveries of cscRNAs, even though cscMap only uses the regularly un-mappable RNA-seq reads to recover the cscRNA species. We tested whether the cscRNA reads can be mapped back to just one template strand of the genome with relatively low mapping scores. Most of these efforts failed, i.e., generally negligible proportions of the cscRNA reads could be re-aligned conventionally to just one strand  at the junction sitesand Fig. , which hand Fig. .47. These reads were supplemented into the real RNA-seq datasets as inputs of cscMap. As shown in Fig. Furthermore, we generated testing RNA-seq data to benchmark the accuracy of cscMap in mapping the cross-strand junction reads and identifying the cscRNAs species. First, for the sensitivity of cscMap to the cross-strand junction reads, we used the real RNA-seq data in ENCODE supplied with simulated cross-strand junction reads from randomly designed cscRNAs. Similar tests were performed for 10 times, and cscMap exhibited very high sensitivity to these simulated cscRNA reads Fig. . Second,With the technical artifacts ruled out, we then tested whether some of the cscRNA species are abundant in the cells and therefore unlikely to be just biological noise. Here we only looked at the 3013 recurrent cscRNAs from the 271 ENCODE samples. First, by taking the maximum among all the samples, the numbers of the RNA-seq reads covering the junction site of each cscRNA are distributed in a range slightly lower than that of 10,000 randomly selected regular exon–exon junctions Fig. . Such obNext, we assessed the context-dependency of the cscRNAs by counting the overlapping cscRNAs across different samples. 94% of the recurrent cscRNAs were detected in at least two different types of the biological samples Fig. . SeveralA set of cscRNAs has been validated with PCR in multiple cancer cell lines  Fig. , even thDetections of the cscRNA sequences by PCR with the genome DNA from bulk cells were negative. However, it is still possible that the cscRNAs were originated from DNA recombination in a small subset of the heterogeneous cell population. In this case, the DNA recombination may not be detectable by PCR with bulk cells. To obtain cells with a homogenous genetic background, we generated cell colonies from single cells. For 2 cscRNAs, the cDNA and genomic DNA from each of these single-cell originated colonies were then subjected to PCR amplification. As shown in Fig. In addition, considering the potential artifacts during RT-PCR, we also used RNA fluorescence in situ hybridization (RNA FISH), a non-RT based assay, to directly probe the cscRNAs in the cells Fig. . Among 7Finally, to explore the potential biological relevance of the cscRNAs, we focused on the experimentally validated cscRNAs and simply tested the effects of cscRNA knockdown in cancer cells. Knockdown of 6 cscRNAs  Fig. , which dAs introduced above, for a cscRNA, the 3′ end of the upstream RNA fragment (5′ fragment) was ligated to the 5′ end of the downstream RNA fragment (3′ fragment) Fig. . These tTherefore, the two RNA fragments forming a cscRNA are transcription products of the opposite DNA strands within a very small region of the genome. Such bi-directional transcriptions fall in two categories, convergent and divergent transcriptions. Interestingly, vast majority of the cscRNAs are products of convergent transcription Fig. , where hFurthermore, as shown in Fig. Next, around the 5’ and 3’ junction sites of the cscRNAs, we looked into the approximal percentage of the two transcripts from convergent transcription that take part in the cross-strand fusions. For the purpose of comparison, we randomly selected the antisense regions and the 5′ exon ends as background of the 5′ and 3′ cross-strand junction sites, respectively.First of all, taking the 45 samples with the highest numbers of the cscRNAs, for the 5′ transcripts, i.e., the transcripts that contributed the 5′ fragment of the cscRNAs, the average RNA read depth showed a sharp drop at the cross-strand junction sites Fig. . By contHowever, this is not the case for the 3′ transcripts of the cscRNAs. At the 3′ junction sites, the RNA-seq reads originated from the cscRNAs only account for small proportions of all the reads combined Fig. . The ave28. Finally, it is worth noting that most of the 3′ junction sites, if not located at the 5′ exon ends, are located inside the exons or introns with a strong enrichment near the exon ends  Even with the highly conservative settings and multiple stringent filters implemented by the pipeline of cscMap, most of the 3013 recurrent cscRNAs were still identified in at least two different types of the biological samples Fig. . Many ofIn summary, from the systematic survey presented in the manuscript, we show that the non-canonical fusion between the sense and anti-sense transcripts is prevalent in human and multiple other species. We have characterized the basic features related to biogenesis of the cscRNAs, such as their abundances, experimental validations, annotations of the cross-strand junction sites, and other sequence features around the non-canonical junction sites. Next, the biological functions of the cscRNAs, especially their biological relevance in disease and normal physiology, which could be highly complicated and specific to each individual cscRNA, would certainly worth further in-depth investigations. Therefore, we believe that the current study should serve as an insightful resource to inspire and support further elucidation of this additional layer of the transcriptome complexity.The details of all the datasets used in the present study are provided in Supplementary Data zebrafish (GSE73615 and GSE74929), yeast (GSE110413 and GSE99170), C. elegans (GSE79375 and GSE79375), fruit fly (GSE83877 and GSE101603), and E. coli (GSE41190). Datasets from some human samples were also downloaded from the GEO database, including MCF7 cells (GSE94372 and GSE89888), PC3 cells (GSE65112 and GSE48230), and lung tissues (GSE60052 and GSE52248).The datasets from other species were obtained from the GEO database (Supplementary Data The whole-genome DNA-seq data were obtained from the GEO database (GSE48216). The dataset includes 9 breast epithelial cell lines .zebrafish: GRCz11, C. elegans: WBcel235, fruit fly: BDGP6, Saccharomyces: R64-1-1, E. coli: Escherichia_coliK-12_substr.MG1655). The genome annotation files of both human and mouse were obtained from GENCODE , and the annotation files of all the other species were from the Ensemble Genome Browser.The pipeline of cscMap for identification of the cscRNAs is shown in Fig. 62. The program was run with the parameters “-m 2” to control the mismatches no more than 2, “-G genome_annotation.gtf” to use the transcriptome as the reference, and “--library-type fr-firststrand” for strand-specific alignments. All the other default parameters were used. The unmapped reads were then used as inputs of the following steps of cscMap.Paired-end reads were first mapped to the reference genome by TopHat 2.063 was then used for mapping of the normally unmapped RNA-seq reads from above to two different locations. Specifically, the reads were mapped twice at the same time by using the parameters “--norc” and “--nofw”, which allow the program to seek alignments of the reads to the sense and antisense strands separately. This identifies the reads, of which the two fragments were mapped to the two opposite strands on the same chromosome. The minimal length of the read fragment mapped to either of the two DNA strands was set to be 20 nt. The two nucleotides between which the read was truncated into two fragments were defined as the 5′ and 3′ cross-strand junction sites. Finally, when one end of the paired-end reads was truncated into two fragments and consequently mapped to the two DNA strands, the other end (the mate read) was mapped to the upstream of 5’ junction site or the downstream of 3′ junction site, depending on the mapping scenario of the first end that were truncated and mapped.Bowtie 2.0 in its local mode 64) by at least 1 nt were removed. (2) The junction sequences were again aligned to the genome by Blast, and the cscRNAs showing high similarity  to the genome were removed. (3) The cscRNAs showing features of 3′ self-priming or 5′ ligation were removed. Specifically, as shown in Fig. The candidate cscRNAs were then subjected to multiple layers of filters: (1) The cscRNAs of which the sequences around the cross-strand junction sites (±20 nt) overlap with the repeat sequences  for alignments between the DNA sequences and the cscRNA sequences around their cross-strand junction sites (at least 20 nt in up- and downstream of the junction sites). Blast software was used, with the parameters “-subject cscRNA_sequence.fa” to set the cscRNA sequences as reference, “-query DNA_sequence.fa” to align the DNA sequences on the cscRNA reference, and “-outfmt” to customize the output contents. The cscRNAs, of which the RNA fusion events were supported by DNA-seq reads, are marked in Supplementary Data Similarly, the human cscRNA sequences around their cross-strand junction sites (20 nt in up- and downstream of the junction sites) were aligned with the mouse cscRNA sequences. Blast was used with the default parameters. In addition, the 5’ and the 3’ junction sites were also compared between human and mouse cscRNAs. Specifically, with the Blast software, the 5′ fragments (20 nt in upstream of the junction sites) or the 3′ fragments (20 nt in downstream of the junction sites) were aligned to the 5′ fragment (25 nt in upstream of the junction sites) or 3′ fragments (25 nt in downstream of the junction sites) of the mouse cscRNAs, respectively. In addition to the sequence similarity, the 5′ or 3′ junction sites were required to be perfectly aligned between human and mouse cscRNAs. The conserved cscRNA fragments (3′ or 5′), or the full-length are marked in Supplementary Data Fragments per kilobase of transcript per million mapped reads (FPKM) or the raw read counts of the cscRNAs were used as quantifications of the cscRNA abundances. To calculate the FPKM, the length of a given cscRNA was estimated by the spanning region of RNA-seq reads that covered the cross-strand junction sites.2 at 37 °C. HUH7 and A549 cells were cultured in Dulbecco’s modified Eagle medium . PC3 cells were cultured in Roswell Park Memorial Institute 1640 . All the media were supplemented with 10% fetal bovine serum (BI).Cell lines were purchased from American Type Culture Collection (ATCC) and cultured in a humidified incubator with 5% COFor validations of the cscRNAs with PCR, total RNA was extracted and purified with TRIzol (Invitrogen) from the cells by following the manufacturer’s instruction. 2 μg of the purified RNA was subjected to DNase I treatment (Invitrogen), followed by reverse transcription with the High-Capacity cDNA Reverse Transcription Kit (Invitrogen). The cDNA was diluted 10 times in nuclease-free water and used as the templates for PCR amplification. On the other hand, the genomic DNA was extracted with the AxyPrepTM Multisource Genomic DNA Miniprep Kit (Axygen) from the cells. 200 ng gDNA in nuclease-free water was used as the template for PCR amplification.The PCR amplifications were carried out on a thermal cycler with the 1× RealStar Green Power Mixture (GenStar) and 400 nM for each of the forward and reverse primers. The primer sequences are provided in Supplementary Table The PCR products were visualized by electrophoresis in 1% agarose gel with Gel Safe DNA stain (Invitrogen). To obtain the DNA sequences, the PCR products were purified with the Gel Extraction Kit (Vazyme) and then subcloned into the pGEM-T vector (Promega). The plasmids were transformed into DH5α competent cells (Vazyme), which were then spread on an AMP resistance screening LB agar plate . The plates were incubated at 37 °C overnight, and the recombinant cells in the white colonies were collected for Sanger sequencing.In addition to the PCR assays with bulk cells, the similar assays were also performed with cell colonies originated from single cells. Specifically, the cells were grown in dishes and resuspended after trypsinization. The cells were diluted and seeded in 48-well plates. The wells were checked under the microscope, and only the ones with just one cell were used for growing of the cell colonies. After 2–3 weeks, the colonies from single cells were harvested. The RNA and DNA were extracted and purified for the PCR assays as introduced above.66. Specifically, about 0.15 kb fragments covering the cscRNA junction sites were amplified using the primer sets . The fragments were then cloned into pGEM-T (Promega). The cscRNAs probes were synthesized with DIG RNA labeling Mix (Roche) and SP6 or T7 polymerase (Thermo), according to the manufacturers’ instructions.The RNA-FISH assay was performed as previously describedCells were seeded onto a Nunc glass bottom dish, washed with PBS solution, fixed with 4% paraformaldehyde solution for 10 min and then washed with 1× PBS solution for three times at room temperature (RT). Subsequently, the fixed cells were permeabilized with 0.5% Triton X-100 in PBS solution for 5 min and then rinsed with PBS solution. The slides were incubated with prehybridization solution  formamide, 10 mM EDTA (pH 8.0), 100 μg/mL yeast tRNA, and 0.01% Tween-20) at 55 °C for 2 h. The prehybridized slides were then incubated with hybridization solution (prehybridization solution containing 5% (wt/vol) dextran sulfate and 2 μg/mL digoxigein (DIG)-labeled RNA probe at 55 °C for 16 h. After hybridization, the slides were washed twice with prewarmed wash buffer  formamide, and 0.01% Tween-20) at 55 °C for 30 min, and then excess RNA probes were digested by incubating with 10 μg/mL RNase A in NTET buffer (10 mM Tris·HCl (pH 8.0), 1 mM EDTA, 500 mM NaCl, and 0.1% Tween-20) at 37 °C for 1 h. The slides were then washed once with buffer A (2× SSC and 0.01% Tween-20) at 55 °C for 30 min and twice with buffer B (0.1× SSC and 0.01% Tween-20) at 55 °C for 30 min. For detection, the slides were washed with Tris buffered saline solution containing 0.1% Tween-20 (TBST), incubated with 3% (wt/vol) BSA blocking solution in TBST at RT for 1 h, and then incubated with sheep anti-DIG antibodies  diluted (1000 X) in blocking solution at 4 °C overnight. Unbound antibodies were removed by three 15-min washes in TBST. The slides were then incubated with a Donkey Anti-Sheep IgG H&L (Alexa Fluor® 594)  fluorophore-conjugated secondary antibody diluted (1000 X) in blocking solution for 1 h at RT. After washing, the slides were mounted with medium containing DAPI . Fluorescent images were visualized at RT under a microscope (Zeiss LSM980 Airyscan2) equipped with Plan Apochromat ×60/1.4 objective lenses (Zeiss). Imaris 9.6 software (Bitplane) was used for image acquisition and processing. All overlaid images were transferred as high-resolution TIFF files.The siRNAs specifically targeting cscRNAs, PPIB, and non-targeting control siRNA were synthesized by GenePharma  was used to monitor real-time cell proliferation and morphology changes. Cell proliferation was quantified by measuring the occupied area (% confluence) in the cell images over time. The proliferation curves were made with confluence fold change (FC) at different time points in relative to the confluence at time 0.For colony formation assays, 1000 cells were seeded in the 6-well plates and incubated with normal growth medium for 14 days. Then cells were fixed and stained with 0.5% crystal violet for 15 min. Lastly, the colonies were imaged via a camera or a microscope.The wound healing assay was performed to monitor and quantify cell motility. Briefly, cells were seeded in a 96-well plate at 30,000 cells per well and allowed to reach confluence before the surface was uniformly scratched across the center of the well by an Essen wound maker (Essen Bioscience). The wells were then rinsed with fresh medium to remove floating cells, and the wound healing process was monitored continuously in the IncuCyte live-cell imaging system (Essen Bioscience). Images were obtained at each set time point and then analyzed by the IncuCyte scratch wound assay software to quantify wound healing. Data were expressed as wound widths.Further information on research design is available in the Supplementary Figures and TablesDataset 1Dataset 2Dataset 3Description of additional supplementary filesSupplementary SoftwareReporting Summary


The frontal cortex-basal ganglia network plays a pivotal role in adaptive goal-directed behaviors. Medial frontal cortex (MFC) encodes information about choices and outcomes into sequential activation of neural population, or neural trajectory. While MFC projects to the dorsal striatum (DS), whether DS also displays temporally coordinated activity remains unknown. We studied this question by simultaneously recording neural ensembles in the MFC and DS of rodents performing an outcome-based alternative choice task. We found that the two regions exhibited highly parallel evolution of neural trajectories, transforming choice information into outcome-related information. When the two trajectories were highly correlated, spike synchrony was task-dependently modulated in some MFC-DS neuron pairs. Our results suggest that neural trajectories concomitantly process decision-relevant information in MFC and DS with increased spike synchrony between these regions. Deciding actions based on the outcome of past actions is crucial for the survival of animals. The frontal cortex-basal ganglia network is engaged in various forms of decision making involved in adaptive goal-directed behaviors . These sThe MFC is engaged in encoding reward and error signals  and hencEvidence from MFC, or more specifically posterior parietal cortex (PPC) and orbitofrontal cortex (OFC), shows that sequential activation of cortical neuron ensembles, which is termed neural trajectory, underlies decision making . We explWe trained head-restrained rats to lick either left or right spouts for earning reward (positive outcome) and simultaneously recorded multi-neuron activity from the MFC and DS of these rats. Because a rewarded spout was changed intermittently, the rats had to switch their choice responses to maximize reward, exhibiting behavioral responses similar to a typical win-stay lose-shift behavior. Unexpectedly, we found that neural trajectories emerge and evolve highly coherently in both MFC and DS during a period ranging from choice selection to outcome evaluation of the alternative choice task. In some MFC-DS neuron pairs, the number of coincident spikes was modulated by task events when the trajectory evolution displayed an enhanced coherence. Our results suggest that the adaptive control of outcome-based decision making relies on the coevolution of neural trajectories in MFC and DS and that spike synchrony plays a role in this temporal coordination.N = 34, 6 weeks, 200–220 g, Japan SLC, Inc.) were used. Home cages were set in a temperature and humidity controlled environment with lights maintained on a 12-h light/dark cycle. Prior to a primary surgery, rats were handled briefly and habituated to a stainless-steel cylinder in the cages. Animals underwent three surgical procedures. All surgical procedures were operated under sterile circumstances. Rats were anesthetized with 2% isoflurane. Their body temperature was monitored with a rectal probe and maintained at ~37°C on a heating pad during the surgery.All experiments were approved and carried out in accordance with the Animal Experiment Plan by the Animal Experiment Committee of RIKEN. Male Long-Evans rats (ad libitum. Water (10 mL) was supplemented at every weekend. At a second surgery after 19th training session , we injected a retrograde tracer Fluoro-Gold  into the DS in order to confirm if our recording sites corresponded to the MFC sending corticostriatal projection to the DS. A glass micropipette filled with 2% Fluoro-Gold dissolved in 0.1 M of cacodylic acid was installed on a micromanipulator angled medially by 27°. The pipette was inserted through a tiny burr hole drilled in the skull over left hemisphere  so that the pipette tip reached the dorsocentral part of striatum , which rostral agranular medial cortex (AGm) pyramidal neurons directly innervate. Fluoro-Gold was iontophoretically loaded through 7 s pulse of +5.0 μA by 7 s interval for 30 or 60 minutes with an iontophoresis pump . Then, the tiny hole was covered with sterilized spongel and dental silicone sealants . At a third surgery after the 21st training session, two cranial windows (1.2 mm diameter) were made above the DS and MFC  of left hemisphere  and then its dura maters were removed for electrophysiological recordings. For bilateral muscimol injection session, another tiny burr hole was additionally drilled in the skull above MFC of right hemisphere . The cranial windows were covered with the silicone sealants.At a primary surgery, a sliding head-attachment  was implanted on the skull with dual-curing resin cement  and dental resin  as done before in our laboratory . ReferenRats were trained to perform a behavioral task controlled under a customized multiple-rats training system , which enabled training several rats to learn a task paradigm in parallel , 2013. Ta priori without experiencing forthcoming trials  followed by pseudorandom delay period ranged between 0.7–2.3 s A. Withing trials A.Rats were trained to perform the outcome-based choice task through 21 training sessions. After recovery from the first surgery, at a first training session, the rats learned to associate licking a single spout located at left (or right) side with a reward delivery. Once the rats voluntarily licked the spout to acquire a reward, they learned to associate licking another spout set at the opposite side with a reward acquisition through another spout. At this stage, the second delay period after choice was fixed as zero (no delay). At a second training session, the reward-associated spout location was switched after a bunch of accumulated total rewarded trials were observed at each side. Afterwards, as rats reliably showed repetitive rewarded choices rather than random choices, the number of reversals of reward-location increased. Eventually, we set a block reversal condition that rewarded spout location was reversed after accumulated total number of rewarded trials reached 10 within each block. From 13th and 17th sessions, the first and second delay periods were prolonged by extending to 0.7–2.3 s and to 0.3–0.7 s, respectively. After each rat was trained at a training chamber over 19 sessions regardless of task performances, animal was afterwards acclimatized to another task chamber, where electrophysiological recording experiments were conducted, over two more training sessions.N = 34 rats). Multi-neuron activity was simultaneously recorded from MFC and DS of left hemisphere with two 32-channels silicon probes consisting of four shanks (0.4 mm shank separation), on which tetrode-like electrode sites were spaced vertically by 0.5 mm . Each probe was connected to a custom-made headstage installed on either one of two fine micromanipulators  mounted on a stereotaxic frame . A silicon probe was penetrated vertically (depth from pia mater: 1.2 mm) into MFC, specifically into AGm , and the shanks were aligned along midline  at the first and/or second recording sessions. Fifteen out of the 20 rats reached the criterion at the first recording session. For all of 15 rats, the simultaneous recording from MFC and DS was conducted at the first recording session (Day 1). Of the 15 rats, 10 and 5 were tested without and with injection of GABAa agonist muscimol into bilateral MFC at the second recording session (Day 2), respectively. For the muscimol group, the rats were placed in the body restrainer and their heads were fixed on the stereotaxic frame. A microinjection syringe  filled with 0.1% muscimol  dissolved in 0.1 M phosphate-buffered saline  was set Animals were deeply anesthetized with Urethane  and then perfused intracardially with 0.9% chilled saline followed by 4% paraformaldehyde (PFA) dissolved in 0.1 M phosphate buffer (PB). The fixed brain was stored in 4% PFA overnight and then stored in 30% sucrose solution in 0.1 M PB over 2 weeks. Postfixed brains were frozen and coronally sliced into 50-μm-thick serial sections with a microtome Cryostat . The brain sections were stored in 0.1 M PB at 4°C overnight. The brain sections were subject for immunostaining to detect Fluoro-Gold and Neurobiotin. For fluorescent visualizations of Fluoro-Gold labeled neurons and Neurobiotin-loaded neuron, the brain sections were incubated with a rabbit antibody of Fluoro-Gold  at 4°C overnight, followed by incubation with a goat anti-rabbit antibody of Fluoro-Gold conjugated with Alexa-594  together with antibody Strept Avidine Alexa-488  of Neurobiotin over 2 hours. The slices were washed with PB and mount on a slide glass and dried in a shaded box overnight. Fluorescent images were imaged with a fluorescence microscopy  in order to check if Fluoro-Gold labeled neurons were observed around the silicon probe recording locations in MFC and near the Fluoro-Gold injection site in DS as well as if the Neurobiotin-loaded neurons were observed in MFC. After the fluorescent imaging, the brain sections were re-stained the Neurobiotin-loaded neuron using the avidin-biotin-horseradish peroxidase complex  with diaminobenzidine and nickel as demonstrated previously , 2013. FAll of behavioral and neuronal data were analyzed by custom-written MATLAB scripts (MathWorks). Of 34 rats, its reward acquisition probability of 20 rats reached the performance criterion (>75%) at the first and/or second recording sessions. We analyzed behavioral data at the time of reaching the criterion .pr (or ps) is the probability of repeat choice (or switch choice) in the ith trial. The variables R(i) and CL(i) (or CR(i)) indicate presence of reward delivery (0 or 1) and left (or right) choice (0 or 1) in the ith trial, respectively. The coefficients βkR and βkC represent the effect of past rewards and choices, respectively.□β0 is an intercept. These coefficients were calculated at each session. We checked if the coefficient was significantly different from zero by t-test with Bonferroni correction (We estimated effects of past outcome and past choice on current choice (repeat or switch) by the following regression model .(1)\docrrection D.Spike event from multiunit (or juxtacellular) activity was isolated by a custom-made semi-automatic spike-sorting program EToS [12 (or 5) feature dimensions for 4 (or 1) channels; high-pass filter, 300 Hz; time-resolution, 20 kHz; spike-detection interval, >0.5 ms) , 2012. TP = 0.828, MFC/DS = 45/43), R983-r1 , R983-r2 , R985- r1 , R985-r2 , R986-r1 , R991-r1 , R1000-r1 , R1004-r1 , R1005-r1 , R1009-r1 , R1012-r1 .For comparison of neural activity between MFC and DS, we analyzed behavioral and electrophysiological data (isolated cells from multi-neuron activity) in 12 sessions from 10 out of the 20 rats: experiment-id , R982-r1  and Day 2 (muscimol injection) in the muscimol group, we acquired DS neuronal data in Day 1 and Day 2 sessions from 3 rats and 4 rats in muscimol group, respectively. To examine effects of muscimol injection in MFC on neuronal activation of DS cells, we analyzed data from 130 and 77 DS cells in Day 1 and Day 2 sessions, respectively.z-scores by using the mean and SD of control activity, which was obtained from firing rates in randomly chosen 1000 time windows of width 200 ms during the entire task period of each recording session. Then, the z-scores were normalized by the absolute peak value , we calculated mean and standard error of the mean (s.e.m.) of instantaneous firing rate in a 20-ms bin around task events  and the PETHs were smoothed with a Gauss filter (SD = 40 ms). To compare firing pattern across neurons and recording sessions, we transformed the PETHs into ak value E.P < 0.05) for each firing rate in 200-ms wide sliding window by 20 ms increment around choice response (±3 s) (G).Statistical significance of neuronal modulation by choice and outcome was analyzed by a two-way ANOVA with choice (left and right) and outcome (reward and no-reward) factors (e (±3 s) G. To quae (±3 s)  for all e (±3 s) H. To inve (±3 s) F and G.N is the number of units; k normalized by N; Rit represents the mean firing rate of unit i in time bin t and is normalized by the sum of the mean firing rates of all units at time t; < >t denotes the time average. The temporal sparsity provides a measure of entropy of the distribution of normalized activity in any given bin. TS is maximized if a single unit accounts for all the activity in each time bin. The Sequential index approaches unity when the peak times of individual units homogeneously tile the entire duration and only one unit is active at every moment without overlaps between their temporal fields.For quantitative comparison of neural dynamic between MFC and DS, we calculated sequential index which was developed previously . The norN = 12 sessions). Fisher’s linear discriminant (r(t), which is N-dimensional population firing rate vector (N corresponds to the number of population of neurons), separately for Left and Right choice trials in each 200-ms sliding window by 50 ms increment time around choice response (±3 s). Our task is to find a hyperplane, or equivalently the normal vector w of this hyperplane, that best divides the two distributions projected onto the direction of w. If the two distributions have the means and covariance matrices m1, Σ1 and m2, Σ2, respectively, we can obtain w by maximizing the ratio of the between-class variance to the within-class variance,wTm1 and wTm2 are the means of the two projected distributions and wTΣ1w and wTΣ2w are their variances. We can show that S is maximized if w| = 1.All analyses for population activity were separately performed on data sampled at individual recording session  and standard deviations  of the trial-by-trial firing rates in these trials. The discrimination degree between the two clusters is defined as d’ = S.Thus, we can calculate the discrimination function between Left and Right choice trials as r(t) in each 0.2-s wide window (0.2 s increment time) around choice response (±3 s) was projected onto the axis orthogonal to the hyperplane by calculating inner product of r(t) and the normal vector wchoice, which yielded maximal degree of discrimination (d’). For instance, population rate at time t vectors was projected onto the axis orthogonal to “Ch-hyperplane,” tv = < r(t), wchoice>. In this case, we termed the projected rate vector “Ch-projected trajectory.” We performed the same procedure to find “Ot-hyperplane” (woutcome) by classifying trials into rewarded and unrewarded conditions, and to obtain the projected rate vector “Ot-projected trajectory.”Out of the 12 sessions, 9 sessions (7 rats) were subject to further analyses of neural trajectory as those data reached our criterion (cell number ≧25 in both regions per session) in order to keep a sufficient number of cells . The r c and the set of projected population activity tv, respectively, and p(c) is the choice probability (or outcome probability), p(c|tv) is the conditional probability of choice (or outcome) c when tv is observed at time t, and the parenthesis means an averaging over the values of tv. The mutual information was calculated at each time window (bin = 0.2 s) around choice response (±3 s) as demonstrated before (To quantify the difference in Ch-projected trajectory (or Ot-projected trajectory), we calculated the mutual information I  between d before .P = 0.01).A Pearson correlation was performed for Ch-projected trajectory and Ot-projected trajectory between MFC and DS at each time window (bin = 0.2 s) around choice response (±3 s). Because the correlations are sensitive to the number of trials, we randomly selected 431 trials, which was the minimum total number of trials among 9 sessions, and calculated correlation coefficient so that the correlation coefficient was calculated with the same total number of trials sampled. We repeated this procedure 10 times to obtain mean correlation coefficients D and S5.xy-z was calculated in terms of pairwise Pearson’s correlations  as follows:xyR reveals correlation coefficient between MFC and DS neural trajectories calculated above, whereas xzR and yzR reveal the correlation coefficients between MFC and the last outcome (rewarded = 1 or unrewarded = −1) and that between DS and the last outcome, respectively. We repeated this procedure and averaged the partial correlation to get peak correlations (xy-zR) should be different from trajectory correlation (xyR).To examine if the correlations of neural trajectories between MFC and DS were observed by another task event as a confounder, we calculated partial correlation coefficient by taking account of possible influences from the last outcome event (rewarded or non-rewarded). The partial correlation Relations . If the For the 9 datasets which were subject to above neural trajectory analyses, we calculated cross-correlograms (CCGs) under 4 conditions  for all MFC-DS cell-pairs. We used spike times in a 5-s wide window centered on a specific time at which a maximum absolute correlation of neural trajectories was yielded at Ch-hyperplane (or Ot-hyperplane) referred as window Ch (or window Ot) D and S5.t-test with Bonferroni correction, P = 3.73 × 10−15) and that the last choice position most strongly affected the current choice position regardless of the previous outcomes . Thus, the choice pattern of the rats resembled the so-called “win-stay” and “lose-shift,” which is indeed the optimal strategy to maximize reward in the present task.Behavioral data were analyzed for 20 rats that reached the criterion  during electrophysiological recordings. Head-restrained rats were trained to perform an outcome-based two-choice task in which choices were made by licking one of two spouts  and between rewarded and unrewarded outcome periods .Trial-by-trial licking behavior was also modulated by outcome events. Rats generally licked one of the two spouts immediately after the appearance of Go tone in both Left and Right choice trials. Then, after reward delivery, they showed fast and rhythmic licking at around 6 Hz. In contrast, in unrewarded trials the rats showed sparse licking at around 1 Hz during outcome period E. The liTo test whether and how neuronal population activities of the MFC-DS circuit are collectively coordinated during the outcome-based action selection, we recorded multi-neuron activity in the MFC and DS of the left hemisphere using two silicon probes. In addition, we simultaneously performed juxtacellular recordings of Neurobiotin-loaded neurons near the silicon probe inserted into the MFC A. It is n = 468, mean ± SD = 39 ± 18 cells per session) and DS  in 10 rats that yielded sufficiently many neurons for the analysis. The recordings were performed in the left hemisphere while these rats were performing 12 recording sessions of the learned task . The firing rates of these MFC and DS cells were correlated with either choices (Left vs. Right) or outcomes (Reward vs. No-reward) . Many outcome-modulated neurons decreased firing rate during the period of positive outcome , suggesting that neural dynamics were more sequential; i.e., individual neurons have more localized temporal individual neurons have more temporally localized activation patterns, in DS than in MFC.Peri-event time histograms (PETHs) also indicated a prominent decrease in firing rate in the majority of MFC and DS cells after reward delivery E. IntereP < 0.05), where the selectivity index was defined as |AROC—0.5| + 0.5. Thus, choice- and outcome-modulated neurons show similar dynamical evolution in MFC and DS, but their fractions and degrees of selectivity were significantly different between the two brain regions. These results suggest parallelism between MFC and DS in the neural population coding of task-relevant information in this outcome-based choice task.The fractions of choice- and outcome-modulated neurons also evolved similarly in the MFC and DS except that the fraction was greater in the DS than in the MFC for a large portion of task period G. To quaP < 0.05) (P > 0.05)  A. In bot > 0.05) B. These d’, which is the distance between the categorized rate vectors measured orthogonally to such a hyperplane  of population vector  , in the analyses below we omitted three data sets  by using Fisher’s linear discriminant . This method yields a hyperplane that optimally separates datasets belonging to two categories, such as left/right choices or positive/negative outcomes, in the high dimensional space of the instantaneous population rate vectors . We calcperplane A. The pe for DS) B. In pard’ for choice-selective or outcome-selective trajectories peaked around the time of choice response or outcome, respectively  around the response time (t1: 0.2 s before choice response) and then along with the outcome axis (rewarded or unrewarded) during the outcome period (t2: 1 s after choice response). Interestingly, the trajectories evolving in the MFC and DS dynamically changed their relationship during the task. In contrast to that the trial-averaged Ot-projected trajectories gradually increased correlation between MFC and DS around the outcome event (r = 0.50), the trial-averaged Ch-projected trajectories increased correlation only transiently around the choice event (r = 0.62)  C, D, indt-test, P > 0.05) of the averaged partial correlation remained almost unchanged from those of the original averaged correlation. These results suggest that the trajectory correlation between MFC and DS was not simply due to their involvements in the same task event.The observed correlations between MFC and DS neural trajectories were modest and could be a reflection of the fact that both areas were engaged in the same behavioral task. To exclude this possibility, we calculated partial correlations between the neural trajectories remaining after subtracting the influences of the last outcomes (rewarded and unrewarded) on the trajectory correlation . As shown in P > 0.05).The choice and outcome information carried by the neural trajectories also evolved similarly in MFC and DS E. The amThe coherence of the trajectories between MFC and DS varied from session to session . The intt-test, P = 0.366) and reward delivery , while the correlation of Ot-projected trajectories exhibited a peak significantly later than these events  and remained high during the outcome period (P = 0.218 for Ch-projected and 0.546 for Ot-projected trajectories)  in 4 sessions with higher trajectory correlations during choice and outcome periods than in 4 sessions with lower trajectory correlations  C, suggeselations D.To further show the behavioral relevance of neural trajectory, we examined whether the trajectory switched in either or both of MFC and DS when the rats switched their choices after unrewarded trials. In general, the rats switched their choices after a reversal of reward block within a couple of unrewarded trials B, C. We As shown above, neural trajectories in MFC and DS exhibit task event-dependent modulations of coherence. This result raises questions about whether neuronal firing is also temporally correlated between these regions and whether correlated spikes, if any, contributed to neural trajectories. To study these questions, we calculated cross-correlograms (CCGs) of MFC-DS cell pairs in the time window in which MFC-DS correlations of trajectories were maximized see D and S5 Our analysis revealed MFC-DS cell pairs displaying statistically significant spike synchrony in a task-event-dependent manner A. Shufflr = 0.689, P = 0.0398)  E, and th 0.0398) F. These  0.0398) . However 0.0398) . TherefoF = 16.95, P = 0.0179). After the injection (Day 2), one rat showed no significant changes in behavior, but four rats exhibited significantly different reward acquisition probabilities from those prior to the injection (Day 1) . The reward acquisition behavior was impaired in three rats among the four while it was improved in another rat  but not in the control group , and changes in the reward acquisition probabilities between Day 1 and Day 2 were statistically different between the two groups . Because the number of trials was not significantly different between Day 1  and Day 2  in the muscimol group  and because these numbers were not significantly different from those of the control group, the behavioral difference between the two groups was unlikely attributed to the attenuation of their motivations for the task.To confirm the contributions of inter-trajectory correlations between MFC and DS to adaptive control, we examined the effect of bilateral MFC inhibition on the choice behavior and DS-cell activity in 5 rats (muscimol group)  and the median of inter-spike intervals was significantly longer  on Day 2 (n = 77) than on Day 1 (n = 130) (P < 0.05). The proportion of choice-modulated cells around and after choice responses was significantly smaller on Day 2 than on Day 1  (P = 0.569) despite that they displayed different reward acquisition probabilities (P = 0.000625). In particular, selectivity index for choice tended to be higher in the rat with improved performance than in other rats with impaired performance   E, sugges < 0.05) F, top, a < 0.05) F, bottom < 0.05) B. We calbilities G. By con < 0.05) G.Results of muscimol injection suggest that cortical areas other than the MFC also contributed to the present adaptive choice behavior. Since injection needles were placed in the MFC region that frequently contained the corticostriatal neurons retrogradely labeled by Fluoro-Gold , the phaIn this study, we revealed a parallel evolution of MFC and DS neural trajectories during outcome-based choice behavior. These trajectories initially encoded a current choice and later represented the outcome of the choice response. The amount of information carried by the trajectories was not significantly different between MFC and DS, suggesting that these regions share a large portion of task-related information during the task. Furthermore, cross-area spike coincidences in the millisecond range were enhanced when the trajectories were also highly correlated between the two regions in rewarded trials, suggesting that the parallel trajectory evolution in MFC and DS actively participates in coordinating the behavioral task.The strong similarity in neural coding in MFC and DS questions the conventional view of activity selection by the basal ganglia. Along the frontal cortex-basal ganglia axis, the MFC-DS channel is thought to be the stage to process action selection based on past outcomes . A widelThe parallel trajectory evolution in MFC and DS during the present outcome-based decision making is consistent with a previous finding demonstrating a simultaneous and correlated activation of time-dependent ramping activity in MFC and DS during a temporal judgment task . Given tOur findings suggest that MFC and DS cooperate on processing task-relevant information through the temporal coherence of neural activity at both population  and single-cell (spike synchrony) levels. A previous study in freely moving rats demonstrated oscillatory synchronization of the local field potentials at 5–13 Hz across the motor cortex, DS and substantia nigra pars reticulata  and propagation of spindle-like spike-and-wave oscillations along this cortico-basal ganglia pathway. Spikes of individual neurons were phase-locked to the oscillatory local field potentials and synchronized within the cortical-basal ganglia network . SynchroHow was the precise spike synchrony generated between the cortical and subcortical regions recorded in this study? There are several possible explanations based on neural circuit structures. Medium spiny cells in DS receive monosynaptic excitatory inputs from ipsilateral and contralateral MFC  and we iAlternatively, other cortical or subcortical areas could provide common drives. Indeed, we observed synchronous firing in which DS neurons sometimes discharged slightly earlier (submillisecond order) than MFC neurons , which cIn the present outcome-based action selection, bilateral MFC inactivation attenuated, but did not completely eliminate, choice and outcome representations in DS cells , suggestIt was recently shown in the three-layered cortex of the reptile that just a few spikes of single pyramidal neurons can trigger a cascade of firing sequences of neuron ensembles , as was We also observed suppressive activity in DS after reward delivery E, as wasThe Grants-in-Aid for Scientific Research (KAKENHI) from MEXT .Conflict of Interest: The authors declare no competing financial interests.We thank T. Sharp and T. Takekawa for their advice and supports for initial analysis on our data. We are grateful to M. Gilson, P. Goncalves, J. Igarashi, Y. Isomura, T. Kurikawa, and M. Tatsuno for their valuable comments on our results. We thank J. Wickens for his critical reading of our manuscript and Y. Goda for her advices. We thank I. Alsolami for his useful suggestion on data analysis. T.H. and T.F. designed the project. T.H. and R.H. conducted experiments. T.H. analyzed data. T.H. and T.F. wrote the manuscript.20210212_SupplementaryFigures_bhab091Click here for additional data file.


Logistic regression and three types of machine learning (ML) applications were used to analyse the ADCs and clinical factors. Of the 200 patients, 103 had high-grade serous ovarian cancer (HGSOC), and 97 had non-HGSOC . The median ADCmean of patients with HGSOC was significantly lower than that of patients without HGSOCs. Low ADCmean and CA 19-9 levels were independent predictors for HGSOC over non-HGSOC. Compared to stage I disease, stage III disease was associated with HGSOC. Gradient boosting machine and extreme gradient boosting machine showed the highest accuracy in distinguishing between the histological findings of HGSOC versus non-HGSOC and between the five histological types of EOC. In conclusion, ADCmean, disease stage at diagnosis, and CA 19-9 level were significant factors for differentiating between EOC histological types.This retrospective single-center study included patients diagnosed with epithelial ovarian cancer (EOC) using preoperative pelvic magnetic resonance imaging (MRI). The apparent diffusion coefficient (ADC) of the axial MRI maps that included the largest solid portion of the ovarian mass was analysed. The mean ADC values (ADC Ovarian cancer is the eighth most common cancer in women, with 313,959 new diagnoses worldwide in 2020 ,5. On thMagnetic resonance imaging (MRI), which is the preferred imaging modality for ovarian cancer owing to its ability to provide high contrast for the visualisation of soft tissues, has been utilised for the differential diagnosis of HGSOC . HGSOC gMachine learning (ML) was recently introduced to assist in providing an accurate analysis of clinical findings and making treatment decisions ,20. FurtThis retrospective, single-centre study included patients who underwent preoperative pelvic MRI between January 2009 and December 2020 and received a final histopathological diagnosis of EOC from the excised specimen. Patients who were diagnosed with concomitant primary cancers, whose MRIs were not applicable for DWI and ADC mapping, or who did not have a measurable ovarian mass suitable for a confirmatory diagnosis were excluded . The fol2 were obtained, and ADC maps were constructed from these DWIs.MR images were obtained using 3.0 T MRI systems . Conventional pelvic MRI included T2-weighted half-Fourier single-shot turbo spin echo, T2-weighted turbo spin echo, T1-weighted turbo spin echo, and contrast-enhanced T1-weighted turbo spin echo images. DWIs with b-values of 0 and 800 s/mmmean value of each solid portion was derived from the ROI in the PACS. An ROI encompassing the entire ovarian lesion was drawn on the same axial image. First, the ROIs of the solid portion and the entire ovarian mass were independently drawn by a gynaecologist and radiologist. The final ROIs were then selected after a consensus meeting by the two reviewers. The two reviewers together then measured the longest diameters of the entire ovarian mass and the solid portion, identified the location of the ovarian lesion, and assessed the presence of radiologic peritoneal seeding.A gynaecologist with six years of experience in gynaecology and a radiologist with seven years of experience in gynaecological MRI reviewed the pelvic MRIs. All reviewers were blinded to the histological findings. After reviewing the entire sequence of each patient’s MRI scan, the largest axial ADC map of the solid portion of the ovarian cancer was selected to delineate the region of interest (ROI). The ROI in the selected solid portion was manually drawn using the picture archiving and communication system (PACS) . The ADCp < 0.05.The clinical and radiological features of patients with HGSOC and patients with non-HGSOC were compared using the Mann–Whitney U test for nonparametric continuous variables and the chi-squared test for categorical variables. Statistical significance was set at The Kruskal–Wallis test was then performed to assess the differences between the parameters of patients with HGSOC, clear-cell carcinoma (CCC), mucinous carcinoma (MC), endometrioid carcinoma (EC), and low-grade serous ovarian carcinoma (LGSOC). The Mann–Whitney U test and Bonferroni’s method were used for post-hoc analysis of the groups of patients stratified by specific tumours to identify significant differences. A logistic regression model was used to compare patients with HGSOC to those with non-HGSOC to evaluate the predictability of the patients’ MRI findings and their clinical features for histological types of EOC. Differences between factors that were found to be statistically significant by the Mann–Whitney U test were evaluated using logistic regression analysis.mean values between gynaecologists and radiologists was evaluated using the intraclass correlation coefficient (ICC). SPSS software  was used for statistical analysis.A receiver operating characteristic (ROC) curve was generated for patients with HGSOC versus those without HGSOC. The optimal cut-off value, sensitivity, specificity, positive predictive value, and negative predictive value were determined at the point at which the Youden index (sensitivity + specificity −1) was maximal. The area under the curve (AUC) was calculated as follows: the reliability of the ADCRandom forest, gradient boosting machines (GBMs), and extreme gradient boosting (XGBoost) are machine learning (ML) tools used in this study. The algorithms were chosen after initial experiments using the AutoML method, through which a wide range of models were automatically selected, trained, and tuned. The hyperparameters were tuned using the grid search method over tuneable hyperparameters for each algorithm. To calculate representative performance metrics for each model, five trainings, each using 5-fold cross-validation, were performed. Variable importance was extracted, and heatmaps were constructed to compare the effects of each variable on the outcomes of the different types of models. All machine learning procedures were performed in Python and using the H2O.ai API. The mean value of the 5-fold cross validation of the gradient boosting machine tool was used to generate ROC curves. The accuracy was derived from the ratio of the number of correct predictions to the total number of input samples.n = 103) and 97 had non-HGSOC, consisting of CCC (n = 35), MC (n = 33), EC (n = 21), and LGSOC (n = 8). Borderline ovarian tumours (BOTs) were excluded from the original analysis since five of 12 (41.7%) patients with borderline tumours had no measurable solid portion on preoperative MRI. The median age of the patients in the HGSOC group was significantly higher than that of the patients in the non-HGSOC group . Most patients with HGSOC had stage III disease at the time of diagnosis  and most patients with non-HGSOC had stage I disease at the time of diagnosis . The level of CA 125 and prevalence of peritoneal seeding were significantly higher in patients with HGSOC than in those without HGSOC . The CA 19-9 and the diameter of the entire ovarian lesion were lower in patients with HGSOC than in those without HGSOC. The median value of the ADCmean was lower in the patients with HGSOC than in the patients with non-HGSOC   .mean was significantly lower for the patients with HGSOC, at 1.05 × 10−3 s/m2, compared with the values for the other histological types  . However, the median value of the ADCmean of patients with HGSOC was lower than that of patients with LGSOC (1.36 × 10−3 s/m2), but the difference was not significant. Additionally, among patients with non-HGSOC, the ADCmean value of patients with EC was the lowest and increased in the order of LGSOC, MC, and CCC. The only significant difference was noted between EC and MC (p < 0.001)  .p = 0.001). Among the five histological types of EOC, the ratio of the solid portion to the total ovarian lesion was significantly higher in patients with HGSOC than in those with MC . In addition, EC had the highest ratio of the area of the solid portion to the total area of ovarian lesions, and the ratios of CCC, LGSOC, and MC decreased in non-HGSOC; however, the difference was not significant . Among the five histological types of EOC, ADCmean, CA 19-9, and the ratio of the area of the solid portion to the total area of the ovarian lesions were statistically significant factors. However, ADCmean was a significant factor only between EC and MC (p = 0.009), and the ratio of the area of the solid portion to the total area of the ovarian lesions was statistically significant between CCC and LGSOC (p < 0.001) in post-hoc analysis. In the confined early stage, a difference among non-HGSOC patients was not clearly observed. However, the ADCmean and ratio of the area of the solid portion to the total area of ovarian lesions had significant roles in distinguishing each histological type of stage I EOC, similar to the ADCmean of all stages of EOC . Compared to stage I disease, stage III disease was significantly associated with HGSOC   . The AUCectively .mean value and the GBM were 0.87 and 0.93, respectively. The mean value of the five-fold cross-validation of the GBM tool was used to generate the ROC curves. ROC, receiver operating characteristic; GBM, gradient boosting machine; ADCmean, mean value of the apparent diffusion coefficient.The AUCs of the ADCmean and FIGO stages at diagnosis were 34.9% and 25.5%, respectively. Among the five histological types of EOC, the ADCmean value and CA 19-9 level were 24.5% and 12.2%, respectively  . For theectively .mean values of the solid components were significantly correlated with the histological types of EOC, not only for HGSOCs and non-HGSOCs, but also for the five histological types of EOC . In previous studies, the morphological characteristics of MR images, tumour markers, signal intensity of DWI, and ADC values were studied to determine correlations with the histological types of EOC [p < 0.01); however, the study was limited by selection bias, as patients with borderline epithelial tumours comprised 51% of the study patients with type I ovarian cancer [This study demonstrated that the ADCs of EOC ,23. Pi es of EOC . Tanaka n cancer . TherefoAmong the five histological types of EOCs present in our study, HGSOCs exhibited the lowest mean ADC values. This finding can be attributed to the high cellularity of the HGSOCs. A typical characteristic of HGSOC is its compact structure, which consists of a solid mass of cells with marked nuclear atypia . Since EThe relationship between the mean ADC value and cellularity in ECs or CCCs is controversial. Tanaka et al. reported that CCC had a higher proportion of solid components than EC. On the contrary, we found that CCC had a lower proportion of solid components than EC, similar to the results of Ono et al. ,29. AccoThe ratio of the area of the solid portion to the total area of ovarian lesions also differed between the histological types of EOC. The ratio was noted to be significantly different between HGSOC and non-HGSOC, HGSOC, and MC. This result was the same as the result of a previous study that showed that HGSOC had a higher proportion of solid lesions than MC . Howevermean, the ratio of the area of the solid portion to the total area of ovarian lesions, and CA 19-9 were related to different non-HGSOC histological types. Considering this, the result of this study is a cornerstone for preoperative histological evaluation. A multicentre study in only stage I EOC will be necessary to obtain a more precise result.Compared with patients with HGSOC, those without HGSOC were diagnosed at an early stage. Approximately 60% of CCC or EC cases and 80% of MCs are discovered and histologically diagnosed at stage I ,36,37. Tmean values, which were obtained through manually drawn ROIs, other MRI characteristics, and clinical findings, was analysed in this study using ML algorithms. A preliminary evaluation used auto ML to select three types of ML. Compared to the conventional logistic regression analysis performed for HGSOC and non-HGSOC, ML provided more accurate results for the accuracy, AUC, and AUCPR values. AUCPR, a useful method for evaluating groups with imbalanced outcomes, was also calculated in this study [The importance of the ADCis study .Among the patients with HGSOC and non-HGSOC, GBM showed the highest accuracy (0.91), with an AUC of 0.93 and an AUCPR of 0.91. These results were better than those of the conventional logistic regression model of mean ADC value  and simiDeep learning, a subset of ML, has recently been introduced to extract features from MR images without manually drawing the ROI on an image of the tumour. Although the process is time-consuming, this method provides excellent accuracy . Regardimean value was found to be the strongest factor distinguishing between the five histological types of EOC. The percentage importance allowed the identification of the most essential predictor of outcome  was excellent. Fourth, we believe that a larger population will be necessary to incorporate MRI ADC uniformly to advanced stage. Lastly, borderline tumours were not included in this study due to the high percentage  in the non-measurable solid portion on preoperative MRI.Our study was limited in that it was a retrospective, single-centre study. Therefore, there is a possibility of missing clinical data, which may have resulted in bias. Second, only a five-fold validation was performed, instead of external validation. Third, we analysed the impact of the ADCmean value, disease stage at diagnosis, and CA 19-9 level were meaningful factors for distinguishing between the histological types of EOC. These results formed the basis for creating a predictive computer program to distinguish between the histological types of EOC.This study has several strengths. First, it was relatively large compared to previous studies. More histological types of EOC were included, which allowed the differentiation of the five histological types of EOC. Second, ML, which is more accurate than conventional analysis, was applied to compensate for several limitations and evaluate the data in this study. Third, the importance of each factor in distinguishing between histological types of EOC was calculated. The ADCmean value, disease stage at diagnosis, and CA 19-9 level were meaningful factors for distinguishing between the histological types of EOC. Moreover, we also found that ML identified the ADCmean value as predictive of histological types and that ML was superior to conventional logistic regression analysis. Preoperative MRI is mandatory for patients with ovarian cancer.This study showed that the ADC