H-tetrazolium bromide (MTT) microtiter assay. Euphorbia schimperiana, Acokanthera schimperi, Kniphofia foliosa, and Kalanchoe petitiana exhibited potent antiproliferative activity against A427, RT-4, MCF-7, and SiSo cell lines, with IC50 values ranging from 1.85 ± 0.44 to 17.8 ± 2.31 µg/mL. Furthermore, these four extracts also showed potent antiproliferative activities against LCLC-103H, DAN-G, A2780, KYSE-70, HL-60, and U-937 cell lines, with IC50 values ranging from 0.086 to 27.06 ± 10.8 µg/mL. Hence, further studies focusing on bio-assay-guided isolation and structural elucidation of active cytotoxic compounds from these plants are warranted.Medicinal plants have been traditionally used to treat cancer in Ethiopia. However, very few studies have reported the in vitro anticancer activities of medicinal plants that are collected from different agro-ecological zones of Ethiopia. Hence, the main aim of this study was to screen the cytotoxic activities of 80% methanol extracts of 22 plants against human peripheral blood mononuclear cells (PBMCs), as well as human breast (MCF-7), lung (A427), bladder (RT-4), and cervical (SiSo) cancer cell lines. Active extracts were further screened against human large cell lung carcinoma (LCLC-103H), pancreatic cancer (DAN-G), ovarian cancer (A2780), and squamous cell carcinoma of the esophagus (KYSE-70) by using the crystal violet cell proliferation assay, while the vitality of the acute myeloid leukemia (HL-60) and histiocytic lymphoma (U-937) cell lines was monitored in the 3--2,5-diphenyl-2 Cancer is one of the leading causes of death worldwide. Mainly due to the rapid growth in the human population and increment in the prevalence of risk factors associated with economic transition, cancer incidence and death are rising in Africa . MoreoveMedicinal plants have been traditionally used in Ethiopia for the treatment of various diseases, including cancer ,4,5. HowA. schimperi, E. schimperiana, K. foliosa, and K. petitiana—showed negative T/Ccorr. values, indicating relevant cytotoxic activity at 50 µg/mL , and two suspensions (HL-60 and U-937) cell lines. PBMCs were included to compare the results obtained by the HL-60 and U-937 to primary noncancer cells. This should allow an initial assessment of the selectivity of the extracts. Accordingly, concentration-response curves of the extracts against 10 cell lines were generated and IClculated , Table 250 ˃ 50 µg/mL).Selectivity is a desired property of active lead anticancer agents . DiffereA. schimperi: potent activity was observed when the extract of leaves of A. schimperi was tested on all ten cell lines with IC50 values ranging from 1.87 ± 0.4 to 10.31 ± 3.45 µg/mL. This plant is reported to contain acolongifloroside K as its major active principle, as well as cardiotonic glycosides ouabain and acovenoside A as minor constituents; in fact, it is used by Maasai people in east African as an arrow poison [A. schimperi have been reported for their antiviral [w poison . Young lw poison , hepatitw poison , and tonw poison  in Ethiontiviral , antibacntiviral , antiprontiviral , and in ntiviral  activitintiviral .E. schimperiana: the methanolic extract of E. schimperiana showed moderate cytotoxicity activity against the MCF-7 cell line, with an IC50 value of 25.2 µg/mL [Euphorbia (E. turcomanica) also reduced the viability of HT-29 cells, with an IC50 value of 43 µg/mL [E. umbellata latex sap showed potent cytotoxic activity on hepatocellular carcinoma tumor cells (Hepa1c1c7), at IC50 values ranging from 2-12 µg/mL [E. tirucalli, showed cytotoxic activity against a wide range of human cancer cell lines, including esophageal squamous cells (11.08 µM) and pancreatic carcinoma cells (6.84 µM) [O-β-D glucoronside, and 3-methyl-qurecetin-7-O-β-D-glucoronside), isolated from the 80% methanol extract of E. schimperiana, showed antioxidant activities [E. schimperiana showed potent cytotoxic activity against A427, SiSo, and RT-4 cell lines at concentrations ranging from 1.85 ± 0.44 to 3.28 ± 1.2 µg/mL, and moderate cytotoxic activities against A2780 (26.54 ± 18.5 µg/mL)..2 µg/mL . The met43 µg/mL . Similar12 µg/mL . Euphol,tivities . In the K. petitiana: K. petitiana, known for its traditional use as a treatment of breast and skin cancer in Ethiopia [50 values ranging from 2.09 ± 0.43 to 10.41 ± 5.59 µg/mL. Another plant from the same genus, K. crenata, has been reported to possess potent cytotoxic activity, with IC50 values of 2.33 and 28.96 µg/mL against mesothelioma (SPC212) and hepatocarcinoma (HepG2) cell lines, respectively [K. gracilis, on the other hand, showed only weak antiproliferative activity (IC50 = 136.85 ± 2.32 µg/mL) against human hepatocellular carcinoma (HepG2) [K. pinnata, was reported for its potent cytotoxic activity against the human nasopharyngeal (KB) cell line, with the ED50 value of 80 ng/mL [K. tubiflora were reported to displayed strong cytotoxic activity against A549, Cal-27 , A2058 (melanoma), and HL-60 (promyelocytic leukemia) cell lines [Ethiopia , also shectively . The chl80 ng/mL . Furtherll lines .K. foliosa: K. foliosa inhibited the proliferation of all cell lines, with IC50 values ranging from 14.54 ± 4.14 to 27.06 ± 10.8 µg/mL. Knipholone anthrone, a 4-phenylanthraquinone isolated from this plant, has been reported to possess cytotoxic activity against human acute monocytic (THP-1) and promonocytic (U-937) leukemic cell lines, with IC50 value of 0.9 ± 0.09 and 0.5 ± 0.05 µM, respectively [ectively .http://www.theplantlist.org, accessed on 15 April 2021). Each plant materials were shade dried and ground into powder.Different parts of 22 plant species  were col®®, R-200and R-210, Duisburg, Germany) with heating  at 37–40 °C, followed by freeze-drying  the aqueous concentrate. The dried powder (200 g each) was macerated in 1 L (80% methanol) and shaken for 48 h. The macerated plant material was then filtered through Whatman No.1 filter paper by using a Buchner funnel. The crude methanol extracts were concentrated with a rotary evaporator , in a humid atmosphere of 5% CO2 at 37 °C [v/v) heat-inactivated fetal bovine serum  and supplemented with 30 mg/L penicillin and 40 mg/L streptomycin. Cells were incubated in a 5% CO2 humidified incubator , at 37 °C, and passaged weekly.The cancer cell lines in this study, MCF-7, A427, RT-4, SiSo, LCLC-103H, DAN-G, KYSE-70, A2780, HL-60, and U-937 were obtained from the German Collection of Microorganisms and Cell Culture  . The A27at 37 °C . Cells w®®) with 3.2% sodium citrate as an anticoagulant. Whole blood was 1:2 diluted with phosphate-buffered saline (PBS) . Afterward, 10 mL Histopaque®®-1077  were overlaid with 10 mL of the diluted cell suspension and centrifuged without brake (400× g) for 30 min at room temperature. Subsequently, the PBMC layer was transferred to a new 50 mL centrifugal tube, washed with 10 mL PBS , and centrifuged for 10 min at 250× g. The obtained cell pellet was then washed three times with PBS (5 mL) and centrifuged at 250× g for 10 min. After isolation, the PBMCs were cultured in RPMI 1640 , supplemented with 2 mM L-glutamine  1% v/v antibiotics  and 10% v/v heat-inactivated fetal calf serum. The viability and number of cells were assessed by the trypan blue exclusion assay. Trypan blue was added in a 1:1 ratio to the cell suspension and counted with a Neubauer counting chamber. Cell viability was always >90%. Cells were adjusted to a density of 1 × 106 cell/mL for the following MTT assay. The assay was performed as described, with extract concentrations ranging from 50 to 3.13 µg/mL.Peripheral blood from healthy humans was provided by the blood bank of the University Medicine Greifswald. All donors signed informed consent forms that their blood could be used for research purposes, and all samples were randomized so as to conceal the identity of the donors. Blood samples were collected by venipuncture in tubes (BD Vacutainerv/v) was used as a solvent. The plates were incubated for an additional 72 h at 37 °C. After 96 h, the culture medium was discarded and replaced with a 1% glutaraldehyde buffer saline for 20 min and then stored under Dulbecco’s buffer solution (pH 7.4) at 4 °C. On the day of staining, the buffer solution was removed, and the cells were stained with 0.02% crystal violet in deionized water (100 µL/well) for 30 min. Excess dye was discarded by washing the plates for 15 min in fresh water. The cell-bound dye was redissolved in 70% (v/v) ethanol/water and the optical density was measured at λ = 570 nm with a Spectramax 384 Plus plate reader  or a Sunrise plate reader . The IC50 values were calculated by linear least-squares regression of the T/C corr values versus the logarithm of the added extract concentration and extrapolating to the T/C corr. values of 50% [T is the mean optical density (OD) of the treated cells, ODC the mean OD of the controls, and ODC,0 the mean OD of seeded cells at the time the drug was added.Crystal violet assay was used for both primary and secondary screening of extracts, as described previously . This asH-tetrazolium bromide (MTT) assay was used in this experiment to determine the inhibition of the viability of suspension cells by the extracts [The 3--2,5-diphenyl-2extracts . Briefly50 values were calculated with the software GraphPad Prism 7.0a by determining the inflection point of the simulated sigmoidal curves. The results are presented as means ± standard error of mean where appropriate.All tests were independently performed in triplicate. ICA. schimperi, E. schimperiana, K. petitiana and K. foliosa, showed cytotoxic activity against all ten cell lines. Moreover, these extracts possessed selective cytotoxicity toward suspension cell lines (HL-60 and U-937) when compared to their effect on PBMC, consistent with their traditional use in anticancer therapies. In addition to these four plants, C. abyssinica and G. involucrata also showed selective cytotoxic/antiproliferative activities against some of the human cancer cell lines used in this study. These encouraging results have motivated us to begin isolating and identifying the active components of these four plant extracts, which may contain novel lead compounds for the treatment of cancer.The results of this study indicate that crude extracts of 4 out of the 22 plant species have good cytotoxic activity against human cancer cell lines, as depicted in


Cell barcoding by amine-reactive fluorescent dyes enables analogous cell-based multiplex assays, but requires multiple labeling reactions and quality checks prior to every assay. Here we describe generation of stable, fluorescent protein-barcoded reporter cell lines suitable for multiplex screening of antibody to membrane proteins. The utility of this cell-based system, with the potential of a 256-plex cell panel, is demonstrated by flow cytometry deconvolution of barcoded cell panels expressing influenza A hemagglutinin trimers, or native human CCR2 or CCR5 multi-span proteins and their epitope-defining mutants. This platform will prove useful for characterizing immunity and discovering antibodies to membrane-associated proteins.Multiplex immunoassays with acellular antigens are well-established based on solid-phase platforms such as the Luminex Song et al. describe an immunoassay method, which is a multiplex, fluorescence-barcoded cell lines system that enables high-throughput screening of antibodies binding to large number of protein variants in a single-tube manner. They demonstrate the utility of this tool by assessing binding of different antibodies to a panel of influenza hemagglutinin antigens and to epitope/domain map CCR2 and CCR5 antibodies. For many multi-subunit or multi-pass transmembrane proteins, the native conformation is preserved only when the protein is properly expressed on the cell membrane2. For this reason, cell-based binding assays using protein antigens expressed on the cell surface are widely used in antibody screening and characterization3. A major limitation in these assays is that they are single analyte, or singleplex methods, and consequently time-consuming, sample-depleting, and costly when used to screen samples with high numbers (>10) of antigens. Although multiplex immunoassays based on the Luminex® technology are well-established, only acellular antigens can be immobilized on detection beads with this platform.Membrane proteins are often critical antigens in humoral responses to infection and physiologically significant in health and disease. They are, therefore, valuable targets in vaccine design and therapeutic antibody development7. Distinct cell populations are labeled with unique signatures of amine-reactive fluorescent dyes of different emission wavelengths and intensities before pooling for exposure to potentially reactive antibody followed by analysis by flow cytometry. The FCB technique improves the throughput of flow cytometry experiments and minimizes staining variability and antibody consumption. Although this FCB approach offers flexibility in barcoding cell samples from different sources, especially primary cells, barcoding, labeling, and quality assurance procedures are required prior to every experiment. In contrast, a panel of reporter cell lines stably barcoded by endogenous expression of fluorescent proteins (FPs) yet capable of expressing select membrane-associated antigens by transduction, would establish a stable and highly reproducible immunoassay platform. Once established, endogenously barcoded reporter cell lines would greatly simplify the FCB approach.Fluorescent cell barcoding (FCB) was developed as a cell-based multiplexing technique® beads. Individual reporter cell sub-lines, each bearing a unique combination of FPs, were subsequently engineered to express different cell surface proteins to permit their pooling in a cell-based multiplex immunoassay. Flow cytometry was then used to de-multiplex signals from pooled reporter cell lines by serial gating to identify FP combinations. In this proof-of-concept study, we established a 16-plex basic panel and tested the feasibility of expanding to a 256-plex panel of reporter cell lines. We successfully applied this platform to multiplex detection of antibody binding to cell panels expressing various influenza A hemagglutinin (HA) trimers or human CCR2b and CCR5, and several domain-swap or point mutants that define specific protein domains/epitopes.Here we describe a cell-based, multiplex immunoassay platform using reporter cell lines with stable barcoding by FPs. Briefly, a basal cell line was engineered to express different combinations of FPs that can be reliably detected and distinguished in different channels on flow cytometers. This resulted in a panel of FP-barcoded reporter cell lines analogous to barcoded Luminex8, simplifying the processes of genetic modification and assay processing. A drawback of K562 as a reporter cell line is its expression of the Fcγ receptor, CD32A, which binds most human IgG subclasses with sufficient affinity to raise background thresholds  but not A/Christchurch/16/2010(H1N1), an A/California/07/2009(H1N1)-like strain. Antibody HC19, specific for HA A/Hong Kong/JY2/1968(H3N2)21, bound to that HA but not to others. This assay may be an alternative to Luminex bead assays in high-throughput screening of antigen-specific and cross-reactive antibodies secreted in clonal cultures of human B cells from influenza-vaccinated subjects22.Influenza HAs are diverse trimeric membrane proteins. Recombinant HA protein preparation can be challenging as some HA ectodomains are unstable and cell surface expression can be a surer option for obtaining native structure when working with unknown or novel influenza strains. Multiplex immunoassays with collections of cell surface-expressed HA antigens may be useful in influenza vaccine design and immunological monitoring. To demonstrate, we prepared a panel of 12 reporter cell lines expressing the HA trimers of diverse influenza A subtypes and successfully validated their structural integrity with several reference antibodies  are key to many biological functions and are an important class of drug targets, constituting some 34% of US Food and Drug Administration approvals26. The combination of gain- and loss-of-binding readouts across all the ectodomains allowed a semi-quantitative determination of the contribution of each individual ectodomain  in ECL2A domain Fig.  showed tTo expand the established 16-plex reporter cell line panel, additional transductions for more FPs are necessary. Potentially, the accumulations of genetic insertions in the reporter cell genome could impact the expression and processing of preexisting FPs, other transduced proteins (antigens), and impair cell metabolism or proliferation. To address this concern, we generated a reporter expressing a fifth FP, LSSmOrange. Reporter cell lines FP0000 and FP1111, with none or four preexisting FPs, were pooled and transduced to express LSSmOrange. Similar transduction efficiencies and LSSmOrange intensities were achieved in both parental lines  remained capable of the expression, folding, and processing of analyte proteins, a complex membrane protein, human CCR5, was tested as an example analyte. Similar transduction efficiencies and surface CCR5 expression levels were detected based on the four cell lines established above .Homo sapiens sapiens) subject under Duke Institutional Review Board Committee guidelines (IRB Pro00062495). Written informed consent was obtained.Peripheral blood mononuclear cells (PBMCs) were obtained from one human (30. pLV-EF1a-IRES-Puro (Addgene plasmid # 85132) was a gift from Tobias Meyer31. pLB (Addgene plasmid # 11619) was a gift from Stephan Kissler27. pMD2.G (Addgene plasmid # 12259) and psPAX2 (Addgene plasmid # 12260) were gifts from Didier Trono. mScarlet-I-mTurquoise2 (Addgene plasmid # 98839) was a gift from Dorus Gadella32. pLenti-smURFP-T2A-mCardinal (Addgene plasmid # 80348) was a gift from Erik Rodriguez and Roger Tsien33. pMito-miRFP703 (Addgene plasmid # 80000) was a gift from Vladislav Verkhusha15. pLSSmOrange-mKate2 (Addgene plasmid # 99868) was a gift from Marc Tramier34. Plasmid mNeonGreen-C1 was provided by Allele Biotechnology and Pharmaceuticals, Inc., under a license for non-commercial use.pU6-(BbsI)_CBh-Cas9-T2A-BFP (Addgene plasmid # 64323) was a gift from Ralf Kuehn17. Coding sequences for other FPs were cloned from plasmids mentioned above, with EBFP210 from pU6-(BbsI)_CBh-Cas9-T2A-BFP, mTurquoise211 from mScarlet-I-mTurquoise2, mNeonGreen12 from mNeonGreen-C1, mCardinal13 from pLenti-smURFP-T2A-mCardinal, mKate214 and LSSmOrange16 from pLSSmOrange-mKate2, and miRFP70315 from pMito-miRFP703. Coding sequences for cytoplasmic domain truncated mouse CD4 (amino acid (aa) 1–423), CD8a (aa 1–222), and CD86 (aa 1–268) were cloned from splenocyte cDNA samples from one C57BL/6 mouse. Coding sequences for full-length human CCR2b and CCR5, and cytoplasmic domain truncated human CD4 (aa 1–424), CD8a (aa 1–209), CD86 (aa 1–276), and CD154 (aa 1–240) were cloned from PBMC cDNA samples from one healthy donor.DNA template coding FP hmKeima8.5 was synthesized (Genscript) based on published amino acid sequencesDNA templates coding influenza HA antigens A/Christchurch/16/2010(H1N1) (GISAID Accession EPI280344) and A/Texas/50/2012(H3N2) (GenBank Accession KJ942744) were synthesized  based on amino acid sequences in the databases. Plasmid containing coding sequences for influenza HA A/Solomon Islands/3/2006(H1N1)  was provided by Aaron G. Schmidt (Ragon Institute). Plasmids containing coding sequences for other influenza HA antigens used were provided by Stephen C. Harrison , including HA antigens A/Japan/305/1957(H2N2) (GenBank CY014976), A/Hong Kong/JY2/1968(H3N2) (GenBank CY147438), A/American black duck/New Brunswick/00464/2010(H4N6) (GenBank CY138045), A/Viet Nam/1203/2004(H5N1) (GenBank AY818135), A/Taiwan/2/2013(H6N1) (GISAID EPI459855), A/Taiwan/1/2017(H7N9) (GISAID EPI917065), A/northern shoveler/California/HKWF1204/2007(H8N4) (GenBank CY039588), A/Jiangxi/IPB13/2013(H10N8) (GenBank KJ406543), and A/mallard/Wisconsin/10OS3941/2010(H14N6) (GenBank CY133266).® Endo-free Plasmid DNA Mini Kit II, Omega Bio-tek) for mammalian cell transfection. Recombinant sequences in all plasmids used were verified by DNA Sanger sequencing .Standard molecular cloning procedures were followed for plasmid modification and gene cloning. Endotoxin-free plasmids were prepared  were constructed by replacing the U6-loxP-CMV-EGFP-loxP cassettes in plasmid pLB with EF1a promoter, EFS core promoter , or EF1a-IRES-Puro cassettes from plasmid pLV-EF1a-IRES-Puro, respectively. pLB-EXIP was further modified to generate bicistronic vectors pLB-EF1a-IRES-EBFP2, pLB-EF1a-IRES-mTurquoise2, pLB-EF1a-IRES-mCardinal, pLB-EF1a-IRES-mCD4, pLB-EF1a-IRES-mCD8a, and pLB-EF1a-IRES-mCD86 by replacing puromycin-resistant gene in pLB-EXIP with coding sequences for corresponding genes following IRES element. pLB-EFS-IRES-mNeonGreen was generated from pLB-EXIP by replacing EF1a promoter with EFS core protomer and puromycin-resistant gene with mNeonGreen coding sequences.Lentiviral transfer vector plasmids expressing EBFP2, mTurquoise2, mCardinal, mKate2, miRFP703, LSSmOrange, and hmKeima8.5 were generated by cloning of corresponding coding sequences into pLB-EF1a. Lentiviral transfer vector plasmid expressing mNeonGreen was generated by cloning of mNeonGreen coding sequences into pLB-EFS. Human CD4, CD8a, CD86, and CD154 coding sequences  were cloned into pLB-EXIP for puromycin-mediated selection. Human CD8a coding sequences were cloned into pLB-EF1a-IRES-mTurquoise2 and pLB-EF1a-IRES-mCardinal, and human CD86 coding sequences were cloned into pLB-EF1a-IRES-EBFP2 and pLB-EFS-IRES-mNeonGreen. These bicistronic expression vectors can co-express human CD8a/CD86 and FPs, with FPs at a lower expression level driven by IRES element. Coding sequences for influenza HA antigens, human CCR2b, CCR5, and their domain-swapped or point mutants were cloned into pLB-EXIP.HEK 293T and K562 cell lines were purchased from ATCC. HEK 293T cells were cultured in Dulbecco’s modified Eagle’s medium (Gibco) supplemented with 10% heat-inactivated HyClone fetal bovine serum (FBS) (Cytiva), 10 mM HEPES buffer, and 55 µM 2-Mercaptoethanol . K562 and derivative cell lines were maintained in RPMI-1640 medium (Gibco) supplemented with 10% heat-inactivated HyClone FBS, 10 mM HEPES buffer, 1 mM sodium pyruvate, 1× MEM NEAA, 55 µM 2-Mercaptoethanol, 100 units/ml penicillin, and 100 µg/ml streptomycin . For all K562-derivative cell lines, monoclonal cell lines were established by single-cell sorting (see below) and used in binding assays in this study.CD32A exon 1 were designed with the online tool (http://crispr.mit.edu). sgRNAs used in this study were sgRNA-hCD32A-1 (5′-AGCAGCAGCAAAACTGTCAA-3′), sgRNA-hCD32A-2 (5′-ATGTATGTCCCAGAAACCTG-3′), and a negative control sgRNA (5′-TGTCATGCGTCACTTAGTGC-3′). Corresponding DNA oligos were synthesized and cloned into plasmid pU6-(BbsI)_CBh-Cas9-T2A-BFP. K562 cells were transfected with CRISPR-Cas9 targeting plasmids using Lipofectamine 3000 Transfection Reagent (Invitrogen). Ninety-six hours after transfection, the cells were collected for flow cytometry analysis and single-cell sorting (see below).Single-guide RNAs (sgRNAs) targeting human g for 45 min at 32 °C. For transductions with pLB-EXIP-based vectors, cells were selected with puromycin  between 3 and 7 days after transduction. Seven days after transduction, the cells were collected for flow cytometry analysis and single-cell sorting (see below).Lentiviral transfer vector plasmids were co-transfected into HEK 293T cells with packaging plasmids pMD2.G and psPAX2 using Lipofectamine 3000 Transfection Reagent (Invitrogen). Forty-eight hours after transfection, culture supernatants were collected and filtered through 0.45 µm polyvinylidene difluoride membrane filters (Millipore). K562-derivative cell lines were transduced with the filtered supernatants containing lentiviral vectors by spinoculation at 1000 × 18, S5V2-2919, CH6720, and HC1921 were prepared as recombinant human IgG1 antibodies as described35.Monoclonal antibodies used in this study included the following: FITC-conjugated anti-human CD32A , hCD32-APC , hCD16-PE , hCD32-PE , hCD64-PE , hCD4 , hCD8a , hCD86 , hCD154 , mCD8a-PerCP-eFluor710 , mCD86-PE-Vio770 , mCD4-APC-Fire750 , hCD58-PE , HA-tag , hCCR2 , hCCR2 , hCCR5 , hCCR5 , hCCR5 , hCCR5 , and hCCR5 . Isotype control antibodies included the following: Mouse IgG1 κ-isotype control (Rockland 010-001-330), Human IgG1κ , and Human IgG1 Lambda . Secondary antibodies included the following: Goat Anti-Human IgG-PE (Southern Biotech 2040-09), Goat Anti-Mouse IgG, and Human ads-PE (Southern Biotech 1030-09). Influenza HA-specific antibodies FI6g for 2 min at 4 °C, and resuspended in staining buffer . After incubation with antibodies at 4 °C in the dark for 30 min, cells were washed with staining buffer and resuspended in staining buffer for either secondary staining following the same procedure above or stored on ice for flow cytometry analysis or single-cell sorting. For cell counting, CountBright™ Absolute Counting Beads (Invitrogen) were added into cell suspensions prior to flow cytometry analysis.Cultures of K562 and derivative cells were collected, centrifuged at 300 × Flow cytometry analysis was carried out using either BD FACSCanto II cytometer (Duke Cancer Institute Flow Cytometry Shared Resource) or BD LSR II cytometer (The Duke Human Vaccine Institute (DHVI) Research Flow Cytometry Facility). Single-cell sorting was performed with BD Aria II (The DHVI Research Flow Cytometry Facility). The bulk cell line after transfection or transduction were stained with corresponding antibodies and single cells expressing FP or antigen of interest were sorted into 96-well flat-bottom plates containing 100 µl/well of the complete RPMI medium above supplemented with 20% heat-inactivated FBS. Nine days after sorting, healthily proliferating cell clones were transferred into 24-well plates for further expansion. Three days later, individual monoclonal cell lines were validated for the expression of FP or antigen of interest and a single clone with a uniform expression level was selected for further engineering or used as a reporter cell line in immunoassays.FlowJO (Version 10.7.2), Chromas (Version 2.6.6) and Microsoft Office (Version 1808) were used to analyze data and prepare figures for publication.The reproducibility of flow cytometry detections of the expression of FPs and cell surface molecules was confirmed by two or three independently repeated experiments as indicated in figure legends and shown as Further information on research design is available in the Supplementary InformationReporting Summary


The beneficial effects of both combinations are worth further investigation.The isoquinolinamine FX-9 is a novel potential chemotherapeutic agent showing antiproliferative effects against hematologic and prostate cancer cell lines such as B- and T-acute lymphoblastic leukemia and prostate cancer (PC) of different species. Interestingly, FX-9 shows no hemolytic activity and low toxicity in benign adherent cells. The detailed FX-9 molecular mode of action is currently not fully understood. But application on neoplastic cells induces pro-apoptotic and antimitotic effects. Canine prostate cancer (cPC) represents a unique spontaneous occurring animal model for human androgen-independent PC. Human androgen-independent PC as well as cPC are currently not satisfactorily treatable with chemotherapeutic protocols. Accordingly, the evaluation of novel agent combinations bears significant potential for identifying novel treatment strategies. In this study, we combined FX-9 with the currently approved therapeutic agents doxorubicin, carboplatin, the demethylating substance azacitidine as well as further potentially antitumorigenic agents such as dichloroacetic acid (DCA) in order to evaluate the respective synergistic potential. The combinations with 1–5 μM FX-9 were evaluated regarding the effect after 72 hours on cell viability, cell count and apoptotic/necrotic cells in two human prostate cancer cell lines  and a canine prostate cancer cell line (Adcarc1258) representing androgen-dependent and -independent PC/cPC forms. FX-9 in combination with azacitidine decreases cell viability and increases cell death with positive Bliss values. Furthermore, this decreases the cell count with neutral Bliss values on PC-3. Carboplatin in combination with FX-9 reduces cell viability with a neutral Bliss value and increases cell death on LNCaP with calculated positive Bliss values. DCA or doxorubicin in combination with FX-9 do not show synergistic or additive effects on the cell viability. Based on these results, azacitidine or carboplatin in combination with FX-9 offers synergistic/additive efficacy against prostate adenocarcinoma cell lines  FX-9 (3-(p-Tolyl)isoquinolin-1-amine) is a synthesized amino-substituted isoquinoline , antifunin vitro studies on canine mammary cancer cell lines showed therapeutic potential of this agent [Therapeutic approaches using compound combinations have been effectively introduced in several treatment protocols in order to increase therapeutic efficacy. Accordingly, combined applications allow resistance mechanisms to be addressed and the enhancement of drug sensitivity against individual agents . Moreoveis agent , 16. Theis agent . Therapyis agent , 19. Caris agent . It is uis agent  and in dis agent . Dichloris agent . In humais agent . Current®) were developed to lower hypersensitivity reactions but also side effects such as leucopenia and neutropenia [® was withdrawn by the pharmaceutical company [Prostate cancer occurs spontaneously in humans and dogs. A total of 10–20% of human patients develop castration-resistant prostate cancer (CRPC) within five years of diagnosis, and over 84% of CRPC patients have bone metastases . CRPC istropenia . Paccal  company , 31 due  company . Therefo company . In the in vitro on two human prostate carcinoma cell lines and one canine prostate carcinoma cell line at which FX-9 dosage a combination with azacitidine, DCA, doxorubicin or carboplatin could provide synergistic or additive effects.The aim of the study was to investigate FX-9  10 mM stock solution was dissolved in dimethylsulfoxide .Azacitidine  10 mM stock solution was dissolved in DMSO (Merck KGaA).Dichloroacetic acid  was used in ≥ 99% purity and a lot-specific concentration of 1.547 g/mL. A 1 M stock solution was prepared and stored at -4°C for up to four weeks. For the working solution, the DCA stock solution was dissolved in distilled water and adjusted to pH 7 with a sodium hydroxide solution. The final solution was filtered through a 0.22 μm filter.™, Thermo Fisher Scientific GmbH, Darmstadt, Germany), 10% FBS superior , 2% penicillin-streptomycin (Biochrom GmbH)) and divided into aliquots.Doxorubicin  100 μM stock solution and carboplatin  10 mM stock solution were prepared with culture medium  was prepared in a 1 mM stock solution by dissolving 5 mg solid DHT in 17.21 mL ethanol absolute (EtOH).2 cell culture flasks in medium 199 (Gibco™) with 10% FBS superior (Biochrom GmbH) and 2% penicillin-streptomycin (Biochrom GmbH). The cell cultures were cultivated at 37°C and 5% CO2 in a humidified atmosphere.TiHoDProAdcarc1258 (Adcarc1258) is a canine cell line, derived from a prostate adenocarcinoma of a ten-year-old male Briard , 34. LNC® AQueous One Solution Cell Proliferation Assay . Cells were seeded in 96 well plates at a density of 7,500 cells per well and allowed to adhere overnight. Cells were exposed to 1 μM, 2 μM, 3 μM, 4 μM and 5 μM FX-9 single application and in combination with 3 μM azacitidine, 3 mM DCA, as well as cell line specific dosages of 75 nM (Adcarc1258)/100 nM doxorubicin (PC-3 and LNCaP) or 20 μM (Adcarc1258)/60 μM (PC-3)/80 μM (LNCaP) carboplatin. Single applications of Azacitidine, DCA, doxorubicin and carboplatin were tested at the concentrations outlined above. After 72 hours, 100 μL of MTS -5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt) dissolved in culture medium were added to each well. After two hours of incubation, the absorbance at 490 nm was measured with the Multi-Mode Reader Synergy 2 . The amount of formazan produced by the cells by reducing MTS is proportional to the viability of the cells. The mean value of four wells was used per concentration and per experiment. The experiment was performed in triplicates.Metabolic activity was measured using the CellTiter 96P) with the observed effect (EO). EP is calculated according to the formula: EP = Ea + Eb—EaEb. Ea and Eb represent the effects of the single application of the agents. The difference between the predicted and observed effect is the Bliss value, which determines the degree of synergy. Bliss values greater than zero indicate a synergistic effect, equal to zero an additive effect and less than zero an antagonistic effect [Synergistic effects of drug combinations were calculated using the Bliss independence model. This mathematical method is used to compare the predicted effect  and counted with an automatic cell counter . The mean value of three wells was used per concentration and per experiment. The experiment was performed in biological triplicate.The three cell lines were seeded in 6-well plates at a density of 100,000 cells per well in culture medium and allowed to adhere overnight. The cells were exposed to 1 μM, 2 μM and 3 μM FX-9 single application and in combination with 3 μM azacitidine or 20 μM (Adcarc1258)/80 μM (LNCaP) carboplatin. Single applications of azacitidine and carboplatin were tested in aforementioned concentrations. The cells were incubated at 37°C and 5% CO® Analyzer 10  was used for the flow cytometric measurements. Data analysis was performed with the software FlowJo 7.6.5 . The mean value of three wells was used per concentration and per experiment. The experiment was performed in biological triplicate.The apoptosis rate was analyzed by flow cytometry in all three cell lines after 72 hours of compound exposure. Adherent cells and culture medium were collected to analyze the non-attached cells as well. Samples were pelleted and resuspended in 250 μL binding buffer  before being filtered through a 70 μm filter. Subsequently, 2.5 μL Annexin V-FITC (PromoCell GmbH) and 0.5 μL TO-PRO-3 iodide  were pipetted onto each sample. MACSQuant2. After incubation, the cells were harvested by TrypLE™ Express Enzyme  and counted with an automatic cell counter . The mean value of three wells per concentration and per experiment was used. The experiment was performed in biological quadruplicate.The cell lines Adcarc1258, PC-3 and LNCaP were seeded in 6-well plates at different seeding densities /15,000 cells (PC-3)/100,000 cells (LNCaP) per well) due to different growth behavior and exclusion of growth inhibition by cell contacts. Cells adhered over night and were exposed to 10 nM DHT  or with Statistical analysis was performed with SAS Enterprise Guide 7.1 . The values of the measurements were tested for normal distribution. The student’s t-test was used for LNCaP and Adcarc1258, and the Wilcoxon test for PC-3 to calculate p-values in the androgen sensitivity experiment. Dunnett’s t-test was used to calculate p-values in the remaining experiments. p < 0.05 was considered statistically significant.A dose-dependent decrease in viability was observed in all cell lines by FX-9 single application starting at 1 μM/2 μM  and 4 μM (PC-3). The combinations with azacitidine, DCA, doxorubicin and carboplatin resulted in a dose-dependent and cell line-specific significant decrease in cell viability compared to the single application of the two combined agents .In detail, the combination of all tested FX-9 concentrations with azacitidine decreased cell viability significant to the respective DMSO-controls in PC-3, LNCaP and Adcarc1258 . CompareAll tested FX-9 concentrations with DCA reduced cell viability in LNCaP and Adcarc1258 compared to the DMSO control, whereas in PC-3, the reduction started at 3 μM FX-9. The combination with DCA decreased the cell viability significantly only with 2 μM FX-9 compared to FX-9 single application in PC-3. FX-9 combinations 3–5 μM (PC-3)/2-5 μM (LNCaP)/1-5 μM (Adcarc1258) with DCA resulted in a reduction in cell viability compared to the single application of DCA.The combination of all tested FX-9 concentrations with doxorubicin decreased the cell viability to the respective DMSO-controls in PC-3, LNCaP and Adcarc1258 significantly. Doxorubicin with 1–2 μM /1 μM (Adcarc1258) FX-9 caused a lower cell viability than FX-9 single applications. FX-9 combinations starting at 4 μM (PC-3) or 3 μM  resulted in reduced cell viabilities compared to a single application of doxorubicin.The combination of all tested FX-9 concentrations with carboplatin decreased cell viability significant to the respective DMSO-controls in PC-3, LNCaP and Adcarc1258. Carboplatin with 1–4 μM (PC-3)/1-3 μM (LNCaP)/1 μM (Adcarc1258) FX-9 decreased cell viability compared to single application of FX-9. In comparison with the single application of carboplatin, no combination with FX-9 in PC-3 and combination with 2–5 μM FX-9 in LNCaP and Adcarc1258 caused a reduction in cell viability.Bliss values for the combination of FX-9 with azacitidine gave positive values for 1 μM FX-9 on Adcarc1258 and 2 μM, 3 μM, 4 μM FX-9 on PC-3 . For 1 μThe concentrations of FX-9 with azacitidine or carboplatin that significantly decreased cell viability with positive or neutral Bliss values in the human cell lines  were further tested using cell count. As an interspecific comparison, the canine cell line Adcarc1258 was also tested. A dose-dependent reduction in cell count after single application of FX-9 was observed starting at 1 μM FX-9 in Adcarc1258, and at 2 μM FX-9 in PC-3 and LNCaP . The comThe concentrations of FX-9 with azacitidine or carboplatin that significantly decreased cell viability with positive or neutral Bliss values in the human cell lines  were further tested using flow cytometry with annexin and TO-PRO-3 iodide staining. As an interspecific comparison, the canine cell line Adcarc1258 was also tested. FX-9 in combination with azacitidine or carboplatin decreased the fraction of vital cells, and increased the fraction of apoptotic cells in all tested cell lines and concentrations significantly compared to the DMSO-control . The fraIn the human cell line PC-3 and the canine cell line Adcarc1258 there was no significant difference in cell growth between the control and presence of DHT . With LNThe aim of this study was to evaluate at which FX-9 dosage a combination with azacitidine, DCA, doxorubicin or carboplatin could provide synergistic or additive effects on human and canine prostate cancer cell lines. Azacitidine, DCA, doxorubicin or carboplatin were tested in concentrations achievable in plasma –41 and sAzacitidine is an FDA-approved  cytidineCarboplatin, which was approved by the FDA in 1989 , is usedWhen comparing the cell viability and cell counting methods, differences are evident. Looking at the results of the cell count assay, the compounds appear to be significantly more effective at many concentrations than the cell viability results suggest. This effect was distinct in PC-3, as the combination of FX-9 with azacitidine showed 20–50 percentage points higher efficacy in the cell number compared to cell viability. This phenomenon is well known for the response of cancer cells to DNA synthesis-targeting agents . ResponsThe measured synergism was selective regarding concentration and cell line. Synergistic or additive effects occurred dose-dependent at low concentrations of FX-9. It is possible that FX-9 exhibits a dose-dependent mechanism of action, leading to synergism or additivity with azacitidine or carboplatin at low concentrations. There is evidence that antimitotic agents cause drug- and concentration-dependent variation in cell response . PaclitaProstate cancer in humans is usually dependent on androgen and is treated by hormone deprivation therapy. CRPC develops from this disease despite therapy . The twoIn conclusion, FX-9 provides a dose-dependent selective synergistic potential with azacitidine and carboplatin. The cause of this selective potential should be further investigated regarding possible dose-dependent mechanisms of action. The variance between cell lines after exposure to FX-9 supports the tumour-specific use of chemotherapeutic agents for optimal treatment of prostate cancer . FurtherS1 FigPC-3 was stained with Annexin V-FITC and TO-PRO-3 iodide after exposure to FX-9 and azacitidine. Cells in area Q4 were counted as vital, cells in Q3 as apoptotic, and cells in Q1 and Q2 as necrotic.(TIF)Click here for additional data file.S2 FigAdcarc1258 was stained with Annexin V-FITC and TO-PRO-3 iodide after exposure to FX-9 and azacitidine. Cells in area Q4 were counted as vital, cells in Q3 as apoptotic, and cells in Q1 and Q2 as necrotic.(TIF)Click here for additional data file.S3 FigLNCaP was stained with Annexin V-FITC and TO-PRO-3 iodide after exposure to FX-9 and carboplatin. Cells in area Q4 were counted as vital, cells in Q3 as apoptotic, and cells in Q1 and Q2 as necrotic.(TIF)Click here for additional data file.S4 FigAdcarc1258 was stained with Annexin V-FITC and TO-PRO-3 iodide after exposure to FX-9 and carboplatin. Cells in area Q4 were counted as vital, cells in Q3 as apoptotic, and cells in Q1 and Q2 as necrotic.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Chronic neutrophilic leukemia (CNL) is a rare myeloproliferative neoplasm that is genetically characterized by the absence of both the Philadelphia chromosome and BCR-ABL1 fusion gene and the high prevalence of mutations in the colony-stimulating factor 3 receptor (CSF3R). Additional disease-modifying mutations have been recognized in CNL samples, portraying a distinct mutational landscape. Despite the growing knowledge base on genomic aberrations, further progress could be gained from the availability of representative models of CNL. To address this gap, we screened a large panel of available leukemia cell lines, followed by a detailed mutational investigation with focus on the CNL-associated candidate driver genes. The sister cell lines CNLBC-1 and MOLM-20 were derived from a patient with CNL and carry CNL-typical molecular hallmarks, namely mutations in several genes, such as CSF3R, ASXL1, EZH2, NRAS, and SETBP1. The use of these validated and comprehensively characterized models will benefit the understanding of the pathobiology of CNL and help inform therapeutic strategies. Chronic neutrophilic leukemia (CNL) is a distinct but rare myeloproliferative neoplasm that is BCR-ABL1 negative. CNL is diagnosed on the basis of neoplastic expansion of granulocytic cells and is also included in the WHO classification of hematological malignancies . EpidemiCSF3R is the receptor for colony-stimulating factor 3 (previously referred to as granulocyte colony-stimulating factor) which is known to play a key role in the growth and differentiation of granulocytes . MutatioThe CSF3R mutations fall into two classes: the so-called truncation mutations (a premature truncation of the cytoplasmic tail of the CSF3R) and membrane proximal mutations (point mutations in the extracellular domain), most commonly T618I ,16. MiceThese data suggest that high-frequency oncogenic mutations in the CSF3R are a defining molecular abnormality of CNL and thus clearly represent a major diagnostic criterion ,3. RecenThe survival of CNL patients is dismal and the therapeutic options are limited and do not exhibit proven disease-modifying benefits . This unPreviously, a pair of cell lines was established from a patient with CNL, albeit at different time points during disease progression ,23. ThesMost importantly, both cell lines carry the telltale CSF3R mutation, specifically the T618I variant B. In addIn summary, a major impediment to further investigation of CNL is the lack of informative and faithful models that allow functional interrogation of driver genes and the impact of the acquisition of additional mutations. To address this knowledge gap, we have highlighted here the existence of such a valuable CNL model in the form of two sister cell lines and have undertaken a global characterization of these cells. The presence of typical molecular hallmarks indicates the use of these cell lines as vital preclinical models in the analysis of CNL pathogenesis and in the search for therapeutics.www.dsmz.de, accessed on 1 January 2021).Sister cell lines CNLBC-1 and MOLM-20 have been established from a patient with CNL. Both cell lines are characterized by a distinct mutation landscape which corresponds to that of primary CNL samples, in particular, they carry the CNL-typical mutations CSF3R, ASXL1, and SETBP1. The cell line MOLM-20 is available from the public cell line repository DSMZ (


Enhancer-promoter interactions (EPIs) play a significant role in the regulation of gene transcription. However, enhancers may not necessarily interact with the closest promoters, but with distant promoters via chromatin looping. Considering the spatial position relationship between enhancers and their target promoters is important for predicting EPIs. Most existing methods only consider sequence information regardless of spatial information. On the other hand, recent computational methods lack generalization capability across different cell line datasets. In this paper, we propose EPIsHilbert, which uses Hilbert curve encoding and two transfer learning approaches. Hilbert curve encoding can preserve the spatial position information between enhancers and promoters. Additionally, we use visualization techniques to explore important sequence fragments that have a high impact on EPIs and the spatial relationships between them. Transfer learning can improve prediction performance across cell lines. In order to further prove the effectiveness of transfer learning, we analyze the sequence coincidence of different cell lines. Experimental results demonstrate that EPIsHilbert is a state-of-the-art model that is superior to most of the existing methods both in specific cell lines and cross cell lines. Promoters and enhancers are two important cis-regulatory elements that control gene transcription . EnhanceIn recent decades, many studies ,14,15,16Therefore, with the development of various high-throughput technologies, computational methods have become usual alternatives for identifying EPIs. Whalen et al.  proposedIn recent years, deep learning methods have been proposed to address the above limitations, which build different neural network architectures to learn potential sequence information. One-hot and its variant high-order encoding are usual encoding methods ,21,22,23Overall, even though these researchers have made considerable progress, some limitations still exist. First, almost all these methods do not take into consideration the spatial position relationship. Therefore, these models cannot extract more information. Second, few studies have focused on the analysis of features extracted by model from the spatial perspective; they only use the quantitative value of AUC (area under the curve) and AUPR  to evaluate prediction accuracy. Third, most recent methods lack generalization capability across different cell line datasets. They obtained satisfactory prediction accuracy when train datasets were the same as test datasets, but performed worse across cell lines. Although EPIHC  and SEPTTo address the above limitations, we have designed a model named EPIsHilbert using Hilbert curve encoding and transfer learning. Many researchers have shown that enhancers interact with promoters through complex spatial positions, such as rotating or folding around. Hilbert curve encoding avoids some loss of the spatial position relationship between an enhancer and a promoter, so it can help improve a model’s performance. In order to explore the sequence features that affect the EPIs and their spatial relationships, we add a class activation map (CAM) visualization to display the frequency of sequence features. The occurrence of some diseases is usually related to genetic elements that control the gene regulation, so sequence features with a high frequency of occurrence can be further used in genetic testing or disease diagnosis. For achieving satisfactory prediction performance across cell lines, we proposed two transfer learning strategies to pre-train the model with data from various cell lines. We further explored the reasons for the conclusions of transfer learning according to the analysis of coincidence degree. Experiment results demonstrated that EPIsHilbert not only improved the accuracy of the model prediction in some target cell lines, but also obtained satisfying performance across cell lines.In this study, we used the same dataset from TargetFinder  and SPEIAll sequences were unified into a fixed length. The length of enhancer sequences is 3000 base pairs (bp), while that of the promoter is 2000 bp. In each cell line, the ratio of negative samples to positive samples is 20:1. Thus, for our dataset, a class imbalance problem existed. We used two methods, over-sampling and under-sampling, to achieve a balanced dataset. The details of the balanced dataset are shown in Here, we proposed a prediction model called EPIsHilbert, using Hilbert curve to encode enhancer and promoter sequences, combining sequence analysis with deep learning and utilizing transfer learning strategies to pre-train the model. The overview of EPIsHilbert contains three main steps: Hilbert curve encoding, network architecture, and transfer learning pre-training.To address the limitation of ignoring spatial position relationships, we propose Hilbert curve encoding , a classi.Unify the representation of DNA bases: ‘A’ is encoded as ii.Confirm the Hilbert curve dimension.iii.The Hilbert representation of sequence: for each base in the sequence, we encode the sequence in order of the Hilbert space to obtain a three-dimensional vector.When using Hilbert curve encoding, as the vector space gradually transforms from low to high-dimensional space, the specified points on the line gradually tend towards a more exact point in the vector space. Therefore, Hilbert curve can convert a one-dimensional sequence into a three-dimensional matrix-vector, as we can see in −4 batch-size 100, and epochs 100. To prevent overfitting, we used early stopping to control the number of epochs and dropouts.After Hilbert curve encoding, the obtained three-dimensional vector can be regarded as an image with multiple channels. As such, we use a simple CNN network architecture as our prediction model to extract sequence features. As shown in Some studies show that using the data of one specific cell line provides a more accurate prediction of EPIs on this cell line than on the other five cell lines. This indicates that the ability to predict EPIs across cell lines is not good. Rao et al.  reportedUsing the Data from Other Cell Lines to Pre-Train a Model. We propose a method of transfer learning by using data from other cell lines. For predicting a specific cell line, after using data from all other cell lines to pre-train the CNN model, we use the cell line-specific data to fully train the model. For any given specific cell line, the transfer learning process is as follows:i.Take the specific cell line as a new cell line, ii.Pre-train the CNN model with the training set, denoted as iii.Train the model on training set iv.Evaluate the model on Using the Data from All Cell Lines to Pre-Train a Model. The second transfer learning strategy we propose is in a strong form, which uses the data from all cell lines to pre-train the model. After pre-training, the convolution layer and the pooling layer are frozen. The subsequent training process no longer learns new features, and only adjusts the model parameters for the target cell line. Note that the training, validation and test sets were generated in the way described in i.Pre-train the CNN model with the training set from all six cell lines or 6–8 epochs.ii.Freeze the convolution layer and pooling layer of enhancer and promoter branches.iii.Train the model on the training set from the specific cell line for 10–15 epochs.iv.Evaluate the model on the test set.EPI prediction is equivalent to a binary classification of sequence prediction where three classification indicators are commonly used: precision, recall, and F1-score. Due to the imbalanced datasets, the performance of our model is assessed by area under the precision–recall curve (AUPR) and receiver operating characteristic curve (AUC). AUPR is the area under the precision–recall curve, reflecting trends between precision and recall, better if close to 1. Similarly, AUC is plotted with false positive rate as the horizontal axis and sensitivity as the vertical axis. Thus, AUC reaches its best value at 1 and its worst at 0.In this section, we demonstrate that the model trained on the specific cell line alone is not applicable to other cell lines. The results of the model on each cell line in terms of AUC and AUPR are shown in We can draw two conclusions based on the above results. One, the model without transfer learning lacks a generalization capability across different cell line datasets. On the other hand, the model trained on one specific cell line has a good predictive ability in that cell line.The model transferred to a new cell line in this section is called EPIsHilbert-transOne. Hence, our proposed EPIsHilbert-transOne clearly outperforms EPIsHilbert across cell lines and also improves the performance on four cell lines for cell line-specific predictions.For the convenience of discussion, our model in this section is denoted as EPIsHilbert-transTwo. The experimental result of EPIsHilbert-transTwo on each cell line in terms of AUPR is shown in i.Set the number of coincident samples ii.Take the other five cell lines as a dataset, iii.For each iv.Calculate the remaining four cell lines in a loop to obtain the number of overlapping EPIs between the target cell line and the other five cell lines.v.Modify the target cell line and repeat the above steps five times to complete the overlap number statistics.After using the strategy of transfer learning, the prediction ability of the model across cell lines was improved, which indicated there may have been common sequence fragments among all cell lines. To demonstrate this hypothesis, we explore the sequence overlap ratio of EPIs between different cell lines. The overlap ratio refers to the sequence similarity of EPIs in different cell lines, and the calculation process is as follows:Therefore, we can infer that, when To consolidate the importance of our study, we compared the performance of our proposed models with the other two typical baseline predictors. The two typical baseline predictors are SPEID  and EPIsThese three models we proposed are suitable for different cell lines. To be specific, EPIsHilbert achieves slightly better performance on NHEK and IMR90, while EPIsHilbert-transOne performs better on K562 and Hela-S3. Moreover, on HUVEC and GM12878 cell lines, we obtain the best prediction result from the EPIsHilbert-transTwo model.Based on the above results, we can infer that NHEK and IMR90 cell lines have more unique features because the results are not ideal. However, the two models using transfer learning perform better on K562, HeLa-S3, HUVEC, and GM12878 cell lines. It suggested that many features that influence EPIs in these four cell lines are also shared in all six cell lines.In our work, the proposed class activation map (CAM)  visualizFor example, the CAM of one enhancer–promoter pair in IMR90 is shown in On the basis of CAM, we combine the heat map with the actual coding sequence to complete statistics on the frequency of features. We analyzed the heat map of enhancer and promoter sequences in 1254 EPIs positive samples of IMR90 cell lines to obtain the representations of features sequence. The statistics of enhancer and promoter feature occurrence frequency in IMR90 are shown in In this section, we counted the frequency of feature occurrences. The prevalence of some diseases is usually related to genetic elements that control gene regulation. Thus, in the field of disease diagnosis, features with a high frequency of occurrence can be used in genetic testing or to study the causes of diseases as an assisting technique.In this article, we proposed a model named EPIsHilbert using only sequence data to predict EPIs. Being different from existing methods, EPIsHilbert is innovative at using the Hilbert curve to encode enhancer and promoter sequences and better preserves the spatial structure of the sequence. Thus, we can utilize sequences information and then obtain more details from them. Experimental results on six cell lines indicated that EPIsHilbert performs better than any existing method with an 0.908~0.983 of AUROC and 0.926~0.988 of AUPR.In order to improve the ability of crossing cell lines, we used two transfer learning strategies to pre-train a CNN model by taking advantage of the data from various cell lines. Applied to the same CNN model, each method is more accurate for cross-cell line prediction than current practice, even if it may lose the specificity of each cell line. We further conducted a similarity analysis of the interactions between enhancers and promoters among cell lines. The result shows that NHEK and IMR90 cell lines contain more specific features, while the other four cell lines have more common features, so the prediction accuracy could be further improved by using transfer learning to pre-train the model.We also created a class activation map to explore the features that affect EPIs and the spatial relationships of these features. On this basis, this paper combines a heat map with actual coding sequences to complete statistics regarding the frequency of features. The features with a high frequency of occurrence have a greater impact on the interactions between enhancers and promoters, and they can be further used in a genetic test for disease diagnosis and treatment.Given the excellent performance of EPIsHilbert, we will continue to improve our approach. We may further explore other deep learning architecture for prediction, such as GCN. Then, we suggest that using EPIs data from more datasets may result in a better performance. Since we only use sequence data, integrating sequence data with epi-genomic data is an expected way to improve performance. Moreover, we expect EPIsHilbert can play an important role in all kinds of sequence prediction tasks.


The phosphoproteome was profiled to a depth of 2003 phosphopeptides, of which 207, 854, and 1335 were identified in MCF10A, MCF7, and MDA-MB-231 cell lines, respectively. Furthermore, 2450 phosphorylation sites were mapped to 855 distinct proteins, covering a wide range of functions. The identified proteins are associated with several diseases, mostly related to cancer. Among the phosphoproteins, we validated four enzymes associated with cancer and present only in sEVs isolated from MCF7 and MDA-MB-231 cell lines: ATP citrate lyase (ACLY), phosphofructokinase-M (PFKM), sirtuin-1 (SIRT1), and sirtuin-6 (SIRT6). With the exception of PFKM, the specific activity of these enzymes was significantly higher in MDA-MB-231 when compared with MCF10A-derived sEVs. This study demonstrates that sEVs contain functional metabolic enzymes that could be further explored for their potential use in early BC diagnostic and therapeutic applications.Small membrane-derived extracellular vesicles have been proposed as participating in several cancer diseases, including breast cancer (BC). We performed a phosphoproteomic analysis of breast cancer-derived small extracellular vesicles (sEVs) to provide insight into the molecular and cellular regulatory mechanisms important for breast cancer tumor progression and metastasis. We examined three cell line models for breast cancer: MCF10A , MCF7 , and MDA-MB-231 . To obtain a comprehensive overview of the sEV phosphoproteome derived from each cell line, effective phosphopeptide enrichment techniques IMAC and TiO Extracellular vesicles (EVs) derived from human cancer cell lines are involved in multiple biological processes in tumor biology, such as modulation of the microenvironment, angiogenesis, sustained growth, and tissue invasion ,4,5,6,7.In order to elucidate the molecular changes that coincide with cancer or discover new biomarkers, it is important to measure protein expression in EVs, followed by the identification of post-translationally modified proteins such as phosphorylated proteins. Protein phosphorylation is the most important post-translational modification that dominates signaling transduction, which plays an essential role for almost all cellular functions and is involved in major regulatory mechanisms of cell signaling networks . Recent Some studies reported the detection of phosphoproteins in biofluids for disease diagnosis ,19,20. FCell lines are commonly used as models in cancer biology since they are easily grown and relatively inexpensive, making them suitable for high-throughput testing and omics studies . Blood iThe aim of this study was to gain insight into the phosphoproteome of sEVs derived from the metastatic BC cell lines MDA-MB-231 and MCF7 and from the non-cancerous cell line MCF10A. Differences in phosphoproteomes were observed between the control cell line, MCF10A, and BC cell lines, MDA-MB-231 and MCF7. We selected and validated four phosphoenzymes: ATP citrate lyase (ACLY), phosphofructokinase-M (PFKM), sirtuin-1 (SIRT1), and sirtuin-6 (SIRT6) identified in both MDA-MB-231 and MCF7 that might be potentially relevant to BC diagnosis.8 cells.Breast cancer epithelial cell lines MDA-MB-231 (ATCC HTB-26) and MCF7 (ATCC HTB-22) and the non-tumorigenic breast epithelial cell line MCF10A (ATCC CRL-10317) were obtained from the American Type Culture Collection (ATCC) and used in this study. MDA-MB-231 and MCF10A cells were cultured as described in our previous work [g for 10 min. Apoptotic bodies were removed by centrifugation at 2000× g using a Sigma13190 rotor (MBI) for 20 min. Samples were then spun at 16,500× g for 1 h using an SW28 Ti rotor  to deplete microvesicles. To pellet sEVs, the same rotor was used to centrifuge samples for 3 h at 100,000× g. Collected sEVs were washed with PBS and centrifuged at 100,000× g for 1 h and finally resuspended in 200 μL PBS and stored at −80 °C. A Bradford protein assay kit  was used to assess sample protein concentrations. The average amount of EV protein from three replicate samples was roughly 22 ± 5, 27 ± 6 and 34 ± 6 μg for MCF10A, MCF7 and MDA-MB-231 cell lines, respectively. These samples were used for proteomic analysis.Small EVs were isolated from cell culture supernatant by UC, as described previously . ConditiThe average particle diameter and concentration of isolated EV samples were measured by the ZetaView PMX-110 . The camera shutter speeds were adjusted to 85 and 40. The instrument was calibrated and focused with 102 nm polystyrene beads .g, and the supernatant was then collected.Isolated sEVs were resuspended in the lysis buffer with a volume ratio of 4:1 (fraction/buffer) with a final concentration of 20 mM HEPES, pH 8.0, 5% glycerol, 0.1% n-Dodecyl-β-D-Maltoside, 0.2 mM DTT, 1.6 M urea, and 1/250 (v:v) phosphatase inhibitors  and gently vortexed for 3 min. The suspension was centrifuged for 1 min at 10,000× 2-phosphopeptide enrichment was carried out according to the manufacturer’s protocol . All enriched phosphopeptide samples were acidified by adding TFA , subsequently desalted , and dried in a vacuum evaporator.Protein samples obtained from the isolated sEVs were reduced, alkylated, digested, and desalted, as described previously . Phospho2O/1% formic acid (v/v), three microliters of sample was injected onto an in-house packed column and eluted for 65 min at a flow rate of 300 nL/min . The ESI+ parameters were set as follows: top speed mode, ion source temperature 250 °C, ion spray voltage 2.1 kV, and a full-scan MS (m/z 350–2000) resolution of 60,000. For collision-induced dissociation (CID), the automatic gain control (AGC) target was set to 5 × 105 for full scans and 1 × 104 for MS/MS scans. Precursor ions were filtered from +2 to +7 charge states within 2 m/z isolation windows. CID in the linear ion trap was performed at a normalized collision energy of 35%. For the higher-energy collision dissociation (HCD) method, the AGC target for precursor ions was set to 5 × 105 with an ion filling time of 150 ms. The highest intensity ions were isolated and fragmented with a normalized collision energy of 32% and detected at a mass resolution of 15,000. The AGC target for MS/MS was set to 5 × 104 with a maximum injection time of 150 ms and a dynamic exclusion of 30 s.To process samples, the Orbitrap Fusion mass spectrometer  coupled to an UltiMate 3000 nanoRSLC  was utilized, as previously described with modified instrument parameters ,7. FolloMS raw files were analyzed with MaxQuant (version 2.0.1.0)  and the MaxQuant output tables for protein groups, peptides, and phosphosites were used for all analyses in R . We onlyDisGeNET protein–disease annotations were retrieved with the disgenet2r R package . Cancer-All MS raw data were submitted to the PRIDE repository (Accession: PXD030424) at the European Bioinformatics Institute.Cells and sEVs were lysed by gentle vortexing in extraction buffer provided by the ACLY Activity Assay Kit . Supernatants were collected and used for the analyses of enzymatic activities. ACLY Activity Assay Kit was used according to the manufacturer’s instructions. The assays were performed at room temperature for 60 min.The phosphofructokinase activity was measured using kits from Sigma-Aldrich (Phosphofructokinase Activity Colorimetric Assay Kit MAK093) according to manufacturer’s instructions. The assays were performed at room temperature in 100 μL of reaction mixture. The phosphofructokinase activity was calculated via the standard curve of the NADH standard at known concentrations. The samples were mixed and incubated for 30 min, and the absorbance was monitored at 450 nm.+. Fluorescence intensities of SIRT1  and SIRT6  were measured using a microplate reader (FilterMax F3 and F5 Multi-Mode Microplate Readers from Molecular Devices). The activity of enzymes was calculated from the assay time between 5 and 10 min.The enzymatic activity of SITR1 and SIRT6 was assessed by using the Fluorescent Screening Assay Kit  following the manufacturer’s instructions. The protein extract from cells and EVs was obtained by treatment with Triton X-100 . The assay was performed in 96-well black microplate  with a reaction volume of 50 μL per well. Briefly, the reaction was started by incubating the protein extract with the reaction mixture containing an acetylated peptide substrate. Samples were incubated for 30 min at 23 °C. Control samples were prepared in absence of NADSDS electrophoresis and Western blot experiments were carried out as previously described .In this work, we carried out a systematic analysis of the phosphoproteome of sEVs derived from MCF10A, MCF7, and MDA-MB-231 cell lines to provide insight into the molecular mechanism of breast cancer. To evaluate the range of measured diameters of sEVs isolated by differential ultracentrifugation, nanoparticle tracking analysis was performed. Isolated vesicles, from the three cell lines, measured 50 to 300 nm in diameter, with an average size of about 125 nm A. Additi2 affinity chromatography. An overview of our experimental strategy is presented in 2 affinity chromatography from all sEVs , the probability of correct site analysis identified a total of 1613 class I (>75% confidence), 774 class II and III (25–75% confidence), and 63 class IV (<25% confidence) phosphosites, respectively [A total of three biological replicates were prepared from MCF10A-, MCF7-, and MDA-MB-231-derived sEVs and examined independently. EVs were buffered with phosphatase inhibitors, digested, and the phosphopeptides were enriched to enhance the identification of low-abundance phosphorylated proteins. In addition, we combined two techniques for the enrichments of phosphopeptides: Fe-IMAC and TiOall sEVs . In addiWe visualized sequence motifs for the phosphorylated amino acids for each cell line fraction . The majIn addition, we used the GPS 5.0 software  to prediTo determine whether identified phosphoproteins are linked to cancer diseases, we annotated proteins using the recent set of disease annotations from the DisGeNET database . PhosphoWe annotated and classified proteins using the KEGG pathway database. Pathways terms such as metabolic pathway, spliceosome, cell cycle, and viral carcinogenesis were highly abundant in MCF7- and MDA-MB-231-derived sEVs, compared with MCF10A-derived sEVs  Table S. InteresACLY, PKFM, SIRT1, and SIRT6 activities were detected in all extracted protein fractions from the three cell lines, as well as their sEVs . The spe2, and fragmentation methods, CID and HCD, were employed to maximize the purification and identification of phosphopeptides. We identified 2450 phosphorylation sites with 60 novel ones. These phosphorylation sites were mapped to 870 distinct proteins, covering a broad range of functions. Many identified phosphoproteins were unique to BC EVs encompassing a variety of signaling, metabolic, and regulatory pathways and cellular processes. The significant difference in the number of phosphoproteins identified from sEVs isolated from MCF10A and either MDA-MB-231 or MCF7 could be due to MCF10A producing a lower number of sEVs. This is supported by studies showing that MCF10A cells produce significantly fewer sEVs and less protein content than MDA-MB-231 [To understand the molecular and cellular regulatory mechanisms important for BC tumor progression and metastasis, we performed a phosphoproteomic analysis of BC-derived sEVs. In our study, we used sEVs from two breast cancer cell lines, MCF7 and MDA-MB-231, and a non-tumorigenic breast cell line, MCF10A. Two enrichment techniques, IMAC and TiOA-MB-231 . MoreoveIn this study, among all groups of kinases, CMGC, CAMKs, STE, AGC, and TKL were predicted to regulate the largest number of phosphorylation events in sEVs C. KinaseIn total, 266 phosphoproteins were associated with cancer in sEVs derived from MCF7 and MDA-MB-231 cell lines. Among them, 78 are related to breast cancer, suggesting that the cargo of phosphoproteins in sEVs may play an important role in cancer progression and metastasis. Moreover, these identified phosphoproteins could serve as biomarkers for BC diagnosis. Therefore, this study identified nine proteins  previousAdvancements in proteomics facilitate discovery of enzymes that can provide an attractive source for cancer biomarker tests. Due to their unique enzyme specificity and selectivity, enzymatic assays are a reliable, simple, and rapid diagnostic method . A phospACLY is a cytoplasmic homologous tetramer composed of four polypeptide chains and acts as a metabolic enzyme involved in fatty acid synthesis in rapidly proliferating cancer cells ,45,46. AExpression levels of phosphofructokinase-M (PFKM) are closely related to the occurrence and development of malignant tumors. To meet the metabolic demands of tumor cells in energy, the activity of PFKM in cancer cells is increased ,54,55,56Sirtuin-1 (SIRT1) is a class-III histone deacetylase (HDAC) enzyme involved in gene regulation, genome stability, apoptosis, autophagy, senescence, proliferation, aging, and tumorigenesis . This enSirtuin-6 (SIRT6) is involved in multiple molecular pathways related to DNA repair, glycolysis, gluconeogenesis, tumorigenesis, neurodegeneration, and cardiac hypertrophic responses . SIRT6 m2 enrichment and CID fragmentation methods were employed for peptide enrichment and fragmentation; the study was based on cell culture lysates and did not identify the four enzymes presented in our work.Previous studies have investigated the phosphoproteome of BC cell lines, as well as their EVs ,71. HoweThe objective of this study, along with previously published works ,7, was tIn this study, we conducted a phosphoproteomic analysis of breast cancer-derived extracellular vesicles from MCF10A, MCF7, and MDA-MB-231 cell lines. In total, 855 distinct phosphoproteins were identified collectively among the cell lines, covering a wide range of functions, most of which are related to cancer. Among these phosphoproteins, we validated four enzymes: ACLY, PFKM, SIRT1, SIRT6. The results demonstrate that the specific activity of PFKM in BC cancer cell lines was not statistically different from the non-cancerous cell line. In contrast, the three phosphorylated enzymes ACLY, SIRT1, and SIRT6 showed a significantly higher specific enzymatic activity in MDA-MB-231 in comparison to MCF10A-derived sEVs. These three enzymes might serve as a potential prognostic biomarker for BC. They have been previously proposed as therapeutic targets for cancer therapy. Thus, our findings justify further investigation of these enzymes as promising drug targets for BC treatment.


Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell membrane mostly relies on the mean-squared displacement plots, much information is lost when producing these plots from the trajectories. Here we employ deep learning to classify breast cancer cell types based on the trajectories of epidermal growth factor receptor (EGFR). Our model is an artificial neural network trained on the EGFR motions acquired from six breast cancer cell lines of varying invasiveness and receptor status: MCF7 (hormone receptor positive), BT474 (HER2-positive), SKBR3 (HER2-positive), MDA-MB-468 , MDA-MB-231 (TN) and BT549 (TN).The model successfully classified the trajectories within individual cell lines with 83% accuracy and predicted receptor status with 85% accuracy. To further validate the method, epithelial–mesenchymal transition (EMT) was induced in benign MCF10A cells, noninvasive MCF7 cancer cells and highly invasive MDA-MB-231 cancer cells, and EGFR trajectories from these cells were tested. As expected, after EMT induction, both MCF10A and MCF7 cells showed higher rates of classification as TN cells, but not the MDA-MB-231 cells. Whereas deep learning-based cancer cell classifications are primarily based on the optical transmission images of cell morphology and the fluorescence images of cell organelles or cytoskeletal structures, here we demonstrated an alternative way to classify cancer cells using a dynamic, biophysical feature that is readily accessible.https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification.A python implementation of deep learning-based classification can be found at Bioinformatics online. In response to stimuli, cells adapt a number of strategies to regulate the signaling pathways of transmembrane receptors, including changing the composition of the plasma membrane . ReceptoD) and the linear size of the compartment (L) from the mean-squared displacement (MSD) plots. While MSD analysis is widely used in the field, much information is lost when turning the raw trajectories into an MSD plot (We have previously used the single-particle tracking (SPT) techniques to study the movement of epidermal growth factor receptor (EGFR) in the membrane and showed that the aberrant spatial dynamics of EGFR are associated with the upregulated EphB2 and Src pathways in advanced prostate cancer Liu D and theDeep learning approaches, such as convolutional neural network (CNN), and machine learning approaches, such as k-nearest neighbor, have become an increasingly important tool in image-based tumor segmentation and analysis . Here weWe first acquired EGFR motions from six breast cancer cell lines which have different gene expression profiles, hormone receptor status and surface receptor status: MCF7, BT474, SKBR3, MDA-MB-468, MDA-MB-231 and BT549. The methods for EGFR labeling and trajectory acquisition were described in our previous publications  Kao et . TN subtdt)] were acquired from the six cell lines at 37°C on an Olympus IX-71 inverted fluorescence microscope equipped with a Hamamatsu sCMOS camera  reassembled trajectories as inputs and output a vector containing the probability distributions of predicted classes. The prediction results were visualized in three different ways: a confusion matrix  optimized using stochastic gradient descent (SGD) with categorical cross-entropy as the loss function . By obsen matrix  S7, a ren matrix  S8 and ak − 1 times. Eventually, five networks (k = 5) were trained, and a number of evaluation metrics were obtained based on the testing dataset, providing an assessment of the model performance. Each network generated a set of prediction results, and the model metrics were averaged over the k steps.The model was first evaluated using a testing dataset within the StratifiedKFold from the scikit-learn library. The testing dataset was hidden from the network until each fold had completed training. The StratifiedKFold approach ensured each reassembled trajectory to be used in the testing dataset once and used in the training dataset Based on the testing set of 396 trajectories from the six cell lines , our modThe model was also trained and tested based on receptor status of the six cell lines , we obtained two confusion matrices  that werTo further investigate the capabilities of the trained models, we tested EGFR trajectories from MCF10A cells  with and without the induction of epithelial–mesenchymal transition EMT,  S1. MCF1We demonstrated that well-trained deep learning models based on ResNet  and fromGenevestigator , it can be extended to investigate single-cell behaviors in the tumor microenvironment and tumor heterogeneity (This work was supported by the Cancer Prevention & Research Institute of Texas [RR160005 to T.E.Y.]. T.E.Y. is a CPRIT Scholar in Cancer Research. H.-C.Y. acknowledges the support of this work by Texas 4000, the Robert A. Welch Foundation [F-1833], National Institutes of Health [GM129617] and National Science Foundation [2029266]. Y.-L.L. is a recipient of the YingTsai Young Scholar Award of China Medical University [CMU108-YTY-01] and also a recipient of the Young Scholar Fellowship Program from the Ministry of Science and Technology in Taiwan [MOST 108-2636-E-039-001].Conflict of Interest: none declared.https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification.The dataset and a python implementation of deep learning-based classification can be found at btab581_Supplementary_DataClick here for additional data file.


Mesothelin (MSLN) overexpression (OE) is a frequent finding in ovarian carcinomas and increases cell survival and tumor aggressiveness. Since cancer stem cells (CSCs) contribute to pathogenesis, chemoresistance and malignant behavior in ovarian cancer (OC), we hypothesized that MSLN expression could be creating a favorable environment that nurtures CSCs. In this study, we analyzed the expression of MSLN and CSC markers SOX2 and ALDH1 by immunohistochemistry (IHC) in different model systems: primary high-grade serous carcinomas (HGSCs) and OC cell lines, including cell lines that were genetically engineered for MSLN expression by either CRISPR-Cas9-mediated knockout (Δ) or lentivirus-mediated OE. Cell lines, wild type and genetically engineered, were evaluated in 2D and 3D culture conditions and xenografted in nude mice. We observed that MSLN was widely expressed in HGSC, and restricted expression was observed in OC cell lines. In contrast, SOX2 and ALDH1 expression was limited in all tissue and cell models. Most importantly, the expression of CSC markers was independent of MSLN expression, and manipulation of MSLN expression did not affect CSC markers. In conclusion, MSLN expression is not involved in driving the CSC phenotype. Mesothelin (MSLN) is a glycosylphosphatidylinositol-linked protein  that is Cancer stem cells (CSCs) have been implicated in different steps of carcinogenesis, chemoresistance and cancer dissemination ,5. In HGSOX2 is a transcription factor that regulates embryonic development and stem cell maintenance ,13. Consin situ expression patterns of MSLN and CSC markers SOX2 and ALDH1 in HGSC tissues and OC cell lines. We also evaluated the expression of those markers in OC cell lines and their corresponding nude mice xenografts manipulated to express MSLN, either by CRISPR-Cas9-mediated homozygous Δ or lentivirus-mediated OE. Double IHC with MSLN and CSC markers was performed to potentially identify specific subsets of CSC-like populations associated with MSLN expression. We hypothesized that the association of high MSLN expression with increased cell survival and peritoneal dissemination of OC cells  might beDespite the high expression of MSLN, we frequently observed focal areas of negative staining in HGSC cases. This represented an ideal setting to see how CSC markers behaved in MSLN-positive and -negative cells. We selected whole sections from 14 HGSCs from a previously published series  to evaluThen, serial sections were evaluated for coexpression of MSLN and CSC markers SOX2 and ALDH1 in the 14 HGSC cases. No clear association was observed in the overall series, as expected, since all cases were positive (76–100%) for MSLN A. In mosThe HGSC case shown in Finally, Most of the OC cell lines used in preclinical studies fail to represent the features of HGSC, namely, by showing limited expression of MUC16 and truncated O-glycans . ThroughMSLN did not show any meaningful difference in CSC marker expression as compared with the corresponding parental cells in both 2D cell cultures , Porto, Portugal, and analyzed in a previous study . A subsehigh (OVCAR3 and OVCAR8) and two MSLNlow (OVCAR4 and BG1) OC cell lines were selected in order to generate ΔMSLN and cells with constitutive MSLN OE, respectively. ΔMSLN was obtained by CRISPR-Cas9 gene-editing technology, and MSLN OE was induced by lentiviral transduction through pUltra-MSLN for biscistronic expression of MSLN and EGFP proteins [MSLN-manipulated cell lines were previously generated in our laboratory . Briefly2. For 3D cultures, 96-well round-bottom plates were coated with polyHEMA (poly(2-hydroxyethyl methacrylate))  at 120 mg/mL in 95% ethanol. Aggregates were generated by plating 4 × 103 cells/well and incubating them at 37 °C and 5% CO2 for 96 h. OC cell lines were cultured under 2D and 3D conditions in RPMI 1640  containing 10% fetal bovine serum (FBS)  and maintained at 37 °C and 5% COAll cell lines were authenticated using short tandem repeat (STR) profiling  and regularly tested for the absence of mycoplasma. IGROV1 was included in our panel of OC cell lines, originally described as representing HGSC but recently suggested as better representing an endometrioid carcinoma with a hypermutated profile and co-occurrence of PIK3CA and PTEN mutations .nu/nu mice) using several OC cell lines manipulated to express MSLN  II: v/v) neutral buffered formalin . After fixation, cells were resuspended in HistoGelTM  according to the manufacturer’s instructions, followed by standard histological processing and paraffin embedding. Each cell line block (donor block) was sectioned and stained with H&E for morphology control.All cell lines were cultured in 2D and 3D conditions and arrayed in a cell microarray (CMA) block designed and constructed as previously described ,49,50. BThe CMA for 2D and 3D cell culture conditions and the tissue microarray (TMA) for nude mice xenografts were designed and constructed by adding one core (1.5 mm in diameter) from each donor block to a recipient paraffin block. Tumor tissue cores were included as controls. After construction, CMA was homogenized at 37 °C overnight and sectioned with a standard microtome at 3–4 µm thickness.TM EnVisionTM Detection System Peroxidase/DAB+, Rabbit/Mouse  kit, which was used according to the manufacturer’s instructions. Briefly, endogenous peroxidase activity was blocked with 3% (v/v) hydrogen peroxide solution . Slides were immunostained with monoclonal antibodies for MSLN , SOX2  and ALDH1  and incubated for 1 h at room temperature (RT). Additional antibodies, CD44v6  and OCT4A , were incubated for 1 h at RT as shown in IHC for HGSC tissues and OC cell lines was performed using a manual system. After deparaffinization and hydration, heat-induced (98 °C) antigen retrieval was performed with citrate buffer solution  or ethylenediaminetetraacetic acid . The detection system used was Dako REALTM G|2 Doublestain System, DAB+/Permanent Red Rabbit/Mouse  kit according to the manufacturer’s instructions. Briefly, endogenous peroxidase activity was blocked, slides were immunostained with the first monoclonal antibody, i.e., SOX2  and incubated for 1 h at RT. This primary antibody was detected using a secondary antibody with HRP-labeled polymer, and visualization of the reaction was performed using diaminobenzidine according to the manufacturer’s instructions. Then, slides were immunostained with the second monoclonal antibody, i.e., MSLN  or ALDH1 , and incubated for 1 h at RT. These second antibodies were detected using a secondary antibody with alkaline phosphatase–labeled polymer, and visualization of the reaction was performed using permanent red solution according to the manufacturer’s instructions. Double IHC for HGSC tissues was performed using the EnVisionIHC for TMAs of nude mice tissues was performed using an automated Ventana BenchMark ULTRAStaining System using the OptiView DAB IHC Detection Kit  according to the manufacturer’s instructions. The dilutions of primary antibodies were the same as mentioned above and were added manually with the Ventana™ BenchMark ULTRA platform, according to the manufacturer’s instructions. These second antibodies were detected using the OptiView™ Universal DAB detection kit . A negative control slide was used in place of the primary antibody to evaluate nonspecific staining using a specific reagent, i.e., rabbit monoclonal negative control Ig . Nuclear staining with hematoxylin was performed, and slides were dehydrated, clarified, and sealed with coverslips using a permanent mounting medium for optical microscope analysis.IHC results were evaluated by four independent observers , who registered the percentage of cells stained (0–10% (considered negative), 11–25%, 26–50%, 51–75% and 76–100%). We observed that 79% of the HGSC cases had an MSLN-negative subpopulation, and this observation could be relevant at a moment when MSLN-targeted therapies are being implemented. CSC markers SOX2 and/or ALDH1 were focally identified in all HGSC cases in a manner unrelated to MSLN expression. Similarly, MSLN expression was not associated with either SOX2 or ALDH1 expression in cell lines, and MSLN OE or Δ in genetically engineered OC cell lines did not influence SOX2 and/or ALDH1 expression. In conclusion, MSLN expression is not involved in driving a microenvironment that nurtures CSCs.


Angiosarcoma (AS) is a rare vasoformative sarcoma, with poor overall survival and a high need for novel treatment options. Clinically, AS consists of different subtypes, including AS related to previous UV exposure (UV AS) which could indicate susceptibility to DNA damage repair inhibition. We, therefore, investigated the presence of biomarkers PARP1 (poly(ADP-ribose)polymerase-1) and Schlafen-11 (SLFN11) in UV AS. Based on experiences in other sarcomas, we examined (combination) treatment of PARP inhibitor (PARPi) olaparib and temozolomide (TMZ) in UV AS cell lines.n = 47) and non-UV AS (n = 96) patient samples and two UV AS cell lines (MO-LAS and AS-M) were immunohistochemically assessed for PARP1 and SLFN11 expression. Both cell lines were treated with single agents PARPi olaparib and TMZ, and the combination treatment. Next, cell viability and treatment synergy were analyzed. In addition, effects on apoptosis and DNA damage were examined.Previously collected UV AS (In 46/47 UV AS samples (98%), PARP1 expression was present. SLFN11 was expressed in 80% (37/46) of cases. Olaparib and TMZ combination treatment was synergistic in both cell lines, with significantly increased apoptosis compared to single agent treatment. Furthermore, a significant increase in DNA damage marker γH2AX was present in both cell lines after combination therapy.We showed combination treatment of olaparib with TMZ was synergistic in UV AS cell lines. Expression of PARP1 and SLFN11 was present in the majority of UV AS tumor samples. Together, these results suggest combination treatment of olaparib and TMZ is a potential novel AS subtype-specific treatment option for UV AS patients.The online version contains supplementary material available at 10.1007/s00432-021-03678-4. Angiosarcoma (AS) is a rare sarcoma with endothelial properties and an incidence of 0.15 per 100.000 persons per year NCIN . AS can Over the past decades, AS patient survival has not improved, emphasizing the need for more effective treatment options ; a major pathway involved in among others repair of DNA single strand breaks (SSBs) generated by reactive oxygen species, ionizing radiation and alkylating agents  monotherapy can already effectively cause cell death  or monoclonal rabbit anti-SLFN11 antibody  in antibody diluent in a humidified chamber overnight at 4 °C. Next, tissue sections were incubated with Poly-HRP-GAMs/Rb IgG  in EnVision™ FLEX Wash Buffer  (1:1) for 30 min at room temperature. Antibody binding was visualized using the EnVision™ FLEX Substrate Working Solution (Dako) for 10 min at room temperature. Finally, slides were counterstained with haematoxylin, dehydrated and coverslipped.In a previous study, AS cases and clinical data were collected via a nationwide query in The Netherlands (1989–2014) using the Netherlands Cancer Registry (NCR) , version 25.0.0.1. This study was performed in accordance with the Code of Conduct of the Federation of Medical Scientific Societies in The Netherlands.2/95% air at 37 °C. TMZ and olaparib were purchased from Selleckchem  and diluted in DMSO for in vitro experiments. Experiments were performed in triplicate.Human cutaneous scalp AS cell line MO-LAS was generously provided by Dr. Masuzawa from the Kitasato University School of Allied Sciences, Sagamihara, Japan .Cell viability was assessed using the MTT assay. Cells were seeded in quadruplicate in a 96-wells plate (3000 cells/well) and allowed to adhere overnight. Cells were then treated in a range of concentrations of TMZ (0–500 µM) or olaparib (0–100 µM) monotherapy for 144 h. Next, cells were incubated with 5 mg/ml MTT  in PBS for 3.5 h. The formed formazan crystals were dissolved in MTT solvent . Absorbance was measured with a dual measurement at 560 and 655 nm using the Bio-Rad iMark plate reader . For each drug the IC50 was calculated using GraphPad Prism software  with increasing dosages of TMZ . Effects on cell viability of the monotherapy and combination therapy were assessed in three independent experiments, using an average fraction of cell viability affected (FA) value for further calculations. Results of the combination treatment are represented in an isobologram, in which the line represents an additive effect (CI = 1.0) of the combination at the given FA value. Points below the line represent synergism (CI < 1.0), points above the line represent antagonism (CI > 1.0) (Chou X- and Y-axis represent the fraction of the dose necessary as a single agent to generate reduction of x% cell viability (D1/2) divided by the portion of the drug in the combination treatment (D1 + D2) necessary to reduce a similar x% cell viability (Dx)1/2. DRI values of > 1.0 indicate a favorable dose reduction in the combination treatment compared to the monotherapy dose. Differences in cell viability following combination treatment were analyzed by two-way ANOVA with Bonferroni post-test using GraphPad Prism software, a p value < 0.05 was considered significant .Drug synergy of combined TMZ and olaparib was assessed by calculation of the combination index (CI) and dose reduction index (DRI) with CompuSyn software (ComboSyn Inc.) using the Chou-Talalay method Chou . Cells w.0) Chou . More sp.0) Chou . The X- g at 4 °C for 15 min. Then protein concentrations of the supernatants were determined with the BCA protein assay system . Of each condition, equal amounts of protein (50 μg) were loaded and run on a 12% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) gel under reducing conditions and subsequently transferred to nitrocellulose membranes. After blocking with Odyssey blockbuffer  in TBS (1:1) at room temperature (RT) for 1 h, membranes were incubated overnight at 4 °C with monoclonal rabbit anti-phospho-Histone H2A.X  . Next, the blots were incubated at room temperature for 1 h with a goat-anti-rabbit fluor 680-conjugated secondary antibody , incubated for 1 h at RT with monoclonal mouse anti-GAPDH  as a loading control and subsequently incubated for 1 h at RT with a goat-anti-mouse fluor 800-conjugated secondary antibody . The fluorescent signals were analyzed with the Odyssey Infrared Imaging System (LI-COR Biosciences) and Odyssey Application Software (version 3.0.30). The experiment was performed in duplicate. In addition, western blot analysis of PARP1 and SLFN11 was performed as described above on the MO-LAS and AS-M cell line, using the monoclonal rabbit anti-PARP1 , monoclonal rabbit anti-SLFN11 , and monoclonal mouse anti alpha Tubulin .Western blot analysis of γH2AX was performed to investigate the induction of DNA damage after single agent and combination treatments with low and high dose TMZ. Protein extracts were purified from the MO-LAS and AS-M cell lines after 48 h with single agent and combination treatment. Cells were incubated in cold RIPA buffer containing protease and phosphatase inhibitors and the lysates were centrifuged at 14,0002 . Apoptotic cells were measured using the CytoFLEX flow cytometer  and the percentage of early  and late (Annexin-V and PI positive) apoptotic cells was calculated using FlowJo software (version 10.0.7).Apoptosis was assessed using the Annexin-V/propidium iodide (PI) double staining apoptosis assay  after single agent treatment and combination treatment. Cells were incubated with Annexin-V-FITC and PI in cell culture medium supplemented with CaClp = 0.002, Supplemental Table 2).A total of 47 UV AS patients were assessed for immunohistochemical expression of PARP1 and SLFN11. Median age was 78 years and median overall survival was 13 months (range 0–194 months). Additional clinical characteristics are described in Supplemental Table 1 PARP1 was expressed in 46/47 (98%) of cases, whereas 80% (37/46) of tumors showed SLFN11 positivity .50-value of 1.99 ± 0.11 µM for olaparib and 110.3 ± 15.2 µM for TMZ , and were more sensitive to TMZ than MO-LAS cells (IC50 66.8 ± 11.9 µM)  or 100 µM (high dose) TMZ for both cell lines. Both cell lines were analyzed after 48 h of treatment, showing an increased level of DNA damage marker γH2AX after the combination treatment with both low and high dose TMZ, compared to the respective single agent treatments  UV AS samples and in both UV AS cell lines, indicating a potential role for PARP inhibition in UV AS. In addition, SLFN11 and PARP1 are present in a large proportion of non-UV AS. As expected, single agent olaparib yielded only modest effects in UV AS cell lines MO-LAS and AS-M. We therefore combined olaparib with the alkylating agent TMZ and demonstrated, synergy between olaparib and TMZ treatment in two UV AS cell lines, with strong synergy (CI < 0.3) in nearly all combinations using 50 and 100 µM TMZ. Combination treatment of olaparib and TMZ effectively induced apoptosis after 72 h and 96 h of treatment. The level of γH2AX, indicative of DNA damage, was significantly higher upon combination treatment compared to any of the single agents.The DRI was > 1 for all combination treatments, indicating that the dosage of both TMZ and olaparib can be reduced in combination treatment compared to the effects of the respective single agent treatments. Given the potential toxicity of the combination treatment, these dose reductions could contribute to better tolerability while maintaining optimal anti-cancer effects. Both UV AS cell lines showed a higher DRI for TMZ compared to the DRI for olaparib, which is in line with previous finding showing that olaparib has a potentiating effect on TMZ  , melanoma and glioblastoma multiforme showed that the combination of a PARPi with TMZ was well tolerated but yielded mostly only modest responses , which was discovered to correlate with sensitivity to DNA damaging agents  may enable the design of a biomarker-driven basket study to investigate PARPi and TMZ combination therapy in cancer patients with SLFN11-positive tumors. Ideally, such a study would only include cancer types in which in vitro results already indicate sensitivity to PARPi and TMZ treatment. Another potentially interesting development lies in the combination of PARPi with immune checkpoint inhibition (ICI), based on increased tumor mutational burden (TMB) and upregulation of Programmed Death-Ligand 1 upon PARP inhibition Below is the link to the electronic supplementary material.