Journal: iScience

Article Title: Replicative senescence induction in single cells is not predicted by telomere length, dysfunction, or oxidation

doi: 10.1016/j.isci.2026.114801

Figure Lengend Snippet: The transition from proliferation to senescence is graded at both the population and the single-cell level (A) Population doubling curve for Hs68 fibroblasts. Highlighted points indicate PD 13, 25, and 32 used in subsequent sections. (B) Schematic representation of CDK2 activity sensor. The sensor localizes to the nucleus when CDK2 is off in both quiescence and senescence; phosphorylation by CDK2 marks cell-cycle commitment and leads to translocation of the sensor to the cytoplasm. NLS, nuclear localization signal; NES, nuclear export signal; S, CDK consensus phosphorylation sites on serine. (C) Heatmaps of single-cell CDK2 activity traces for Hs68 fibroblasts. Each row represents the CDK2 activity in a single cell over time according to the color map (blue, quiescent or senescent; yellow, cycling). C/N, cytoplasmic/nuclear signal intensity. (D) Stacked bar chart quantifying the number of mitoses per cell lineage during the live-cell movie from (C). (E) 130 single-cell traces of CDK2 activity from (C) computationally aligned to the time of anaphase for Hs68 plotted for each replicative age. Traces were colored blue if CDK2 activity remained CDK2 low (C/N below 0.8) for 10+ hours following anaphase. (F) Left; cumulative distribution function (CDF) quantifying the total number of hours spent CDK2 low per cell during the live-cell movie (C). Middle; violin plots showing cumulative CDK2 low time per cell from (C). Right; violin plots showing the longest continuous CDK2 low period per cell track from (C). Cells that never raised their CDK2 activity above 0.8 during the experiment are not shown. (G) PD 13 cells from (C) were split into fast cycling (<40 cumulative hours CDK2 low ), slow cycling (50–70 h), or non-cycling (72+ h) (N.C.) categories. Each image shows senescence biomarker staining in one cell. Cyto area was measured by staining with succinimidyl ester. Scale bars represent 26 μm. (H) Violin plots showing the distributions of senescence marker intensity staining in PD 13 cells for each cycling speed described in (G). px, pixels. (I) Cells from (H) were grouped based on cumulative CDK2 low time and intensities for each individual senescence marker were normalized internally and averaged for each group to illustrate the graded manner in which senescence biomarker signals decrease (Lamin B1 and Hoechst standard deviation) or increase with CDK2 low time. (J) ROC analysis showing the capacity of senescence markers to predict/identify cells withdrawn from the cell cycle for more than 60 h in PD 13 cells. AUC, area under curve. (K) In a “binary” model (left), a single cell will maintain high levels of proliferative activity during cellular aging as it approaches senescence, at which point it ceases to proliferate. In our “gradual induction” model (right), senescence induction takes place over the entirety of the replicative aging process where proliferative activity in a single cell declines slowly as senescence biomarkers and tumor-suppressive activity slowly change. Proliferative activity over the course of a 72 h live-cell movie indicates a cell’s proximity to senescence. Information on replicates is provided in data .

Article Snippet: Hs68 (ATCC CRL−1635, authenticated by ATCC using STR profiling) human neonatal foreskin fibroblast cells were obtained from ATCC.

Techniques: Single Cell, Activity Assay, Phospho-proteomics, Translocation Assay, Biomarker Discovery, Staining, Marker, Standard Deviation

Journal: iScience

Article Title: Replicative senescence induction in single cells is not predicted by telomere length, dysfunction, or oxidation

doi: 10.1016/j.isci.2026.114801

Figure Lengend Snippet: Q-FISH-based telomere length quantification cannot predict senescence proximity in single cells (A) Top, top-down view of a 3D projection image of a nucleus of a single Hs68 PD 13 cell with telomere Q-FISH staining. Middle, visual representation of computational segmentation of individual telomeres and quantification of integrated fluorescence intensity (IFI) per telomere. Bottom, IFI distribution for the above cell. Black circle marks the median. (B) Histograms showing distributions of telomere length metrics for 2,327 Hs68 PD 13 cells. Note that Confocal images of Hs68 PD 13 cells shown here were captured using a Nikon AXR with tunable photomultiplier tube detectors, whereas Hs68 PD 33 and AG16359 cells ( A) were captured separately using a Nikon NSPARC with single pixel photon counter detector arrays. Thus, the data from the two microscopes cannot be directly compared. (C) Boxplots showing the relative telomere length distributions for cells from (B). Cells are ordered according to the cumulative time spent CDK2 low during the time-lapse imaging experiment, indicated by the color map. N.C., non-cycling cells. (D) Violin plots showing the distributions of telomere length metrics in PD 13 cells for each cycling speed described in G. (E) Scatterplots showing the correlation between telomere length metrics and cumulative CDK2 low time in PD 13 cells with linear regression analysis in red. Overlaid contours are colored by data point density. (F) ROC analysis using telomere length distribution features to predict/identify cells withdrawn from the cell cycle for longer than 60 h for Hs68 PD 13 cells. AUC, area under curve. (G) ROC analysis using telomere length distribution features to predict/identify cells withdrawn from the cell-cycle for 72 h for Hs68 PD 33 cells.

Article Snippet: Hs68 (ATCC CRL−1635, authenticated by ATCC using STR profiling) human neonatal foreskin fibroblast cells were obtained from ATCC.

Techniques: Staining, Fluorescence, Imaging

Journal: iScience

Article Title: Replicative senescence induction in single cells is not predicted by telomere length, dysfunction, or oxidation

doi: 10.1016/j.isci.2026.114801

Figure Lengend Snippet: Proximity to replicative senescence is weakly correlated with telomere-associated DDR foci (A) Split violin plots showing the number of telomeric and non-telomeric 53BP1 foci detected in Hs68 cells at PD 13 and PD 33. (B and C) Left, violin plots showing cumulative time spent CDK2 low during the live-cell movie in groups based on the number of 53BP1 foci per cell (C) or the number of telomeric 53BP1 foci per cell (D) in Hs68 PD 13 cells. Right, cells were binned based on cumulative CDK2 low time. Error bars represent the 95% confidence interval for the mean. (D) Left, top-down view of a 3D projection image of a nucleus of a single Hs68 cell with 53BP1 immunofluorescence staining and telomere Q-FISH. Computationally detected telomeric foci are numbered in blue and non-telomeric foci are numbered in yellow. Scale bars represent 5 μm. Middle, close-up 3D view of each computationally segmented 53BP1 focus. Blue arrows indicate colocalized telomeres. Right, split violin plots showing the intensity sums of all colocalized vs. not colocalized 53BP1 foci from all cells. Confocal images of Hs68 PD 13 cells were captured using a Nikon AXR with tunable photomultiplier tube detectors, and Hs68 PD 33 and AG16359 cells were captured separately using a Nikon NSPARC with single pixel photon counter detector arrays. (E) Cells were grouped based on cumulative CDK2 low time and segmented 53BP1 foci intensities per cell were summed. (F) Same as (B and C) but using telomeric 53BP1 intensity sums rather than the number of 53BP1 foci. (G) ROC analysis using segmented 53BP1 foci intensity sums per cell to predict/identify cells withdrawn from the cell cycle for more than 60 h (for Hs68 PD 13 and AG16359 cells) or for 72 h (for Hs68 PD 33 cells). A.U.C., area under curve. (H) ROC analysis using nuclear mean 53BP1 intensity per cell to predict/identify cells withdrawn from the cell cycle for more than 60 h (for Hs68 PD 13 and AG16359 cells) or for 72 h (for Hs68 PD 33 cells). This experiment used confocal imaging with at 100X magnification and a 12-bit sensor, whereas J used 10X magnification with a 2×2 bin and a 16-bit sensor, hence the difference in AUC between the two figures. AUC, area under curve.

Article Snippet: Hs68 (ATCC CRL−1635, authenticated by ATCC using STR profiling) human neonatal foreskin fibroblast cells were obtained from ATCC.

Techniques: Immunofluorescence, Staining, Imaging

Journal: iScience

Article Title: Replicative senescence induction in single cells is not predicted by telomere length, dysfunction, or oxidation

doi: 10.1016/j.isci.2026.114801

Figure Lengend Snippet: Telomeric oxidative DNA damage does not increase with cellular aging or senescence (A) Schematic of cell-cycle withdrawal-dependent loss of Ki67 intensity over time. (B) Left, histogram of median Ki67 nuclear intensity in untreated Hs68 PD 28 cells with quintile positions. Middle, multiplexing of Ki67 and 8oxoG immunofluorescence in untreated Hs68 PD 28 cells. Orange arrow indicates a Ki67 high cell (>90 th percentile Ki67 nuclear median), white arrow indicates a Ki67 off cell (<10 th percentile Ki67 nuclear median). Scale bars represent 25 μm. Right, violin plots showing 8oxoG intensity across Ki67 quintiles in PD 28 cells. (C) Population doubling curve for Hs68 with PD 0, 18, 28, 31, and 33 highlighted in blue. (D) Split violins showing the mean 8oxoG intensity in the highlighted ages in (C). (E) Left, top-down view of a 3D projection image of a nucleus of a single Hs68 cell with 8oxoG immunofluorescence and telomere Q-FISH. Right, cartoon depicting 8oxoG centroid-based colocalization quantification strategy. (F) Split violins showing the number of telomeres per cell that colocalize with at least one 8oxoG lesion. (G) Mean fluorescence intensity in the 8oxoG channel within the segmented telomere objects classified as colocalized with at least one 8oxoG lesion. (H) Analyses from (D–G) using Ki67 off cells acquired from the Baltimore Longitudinal Study on Aging repository.

Article Snippet: Hs68 (ATCC CRL−1635, authenticated by ATCC using STR profiling) human neonatal foreskin fibroblast cells were obtained from ATCC.

Techniques: Multiplexing, Immunofluorescence, Fluorescence

Journal: International Wound Journal

Article Title: Curcumin accelerates cutaneous wound healing via multiple biological actions: The involvement of TNF‐α, MMP‐9, α‐SMA, and collagen

doi: 10.1111/iwj.12904

Figure Lengend Snippet: Curcumin down‐regulated MMP‐9 expression in TNF‐α‐treated Hs68 cells via NF‐κB signal pathway. (A) Hs68 cells were incubated with the indicated concentrations of curcumin for 1 hour and then with 3 ng/mL TNF‐α for 23 hours in the continued presence of curcumin. MMP‐9 expression was analysed by immunofluorescence staining. Scale bar = 100 μm. (B) MMP‐9 protein in cell lysates was measured by Western blot. GAPDH was used as the loading control. The data are expressed as a fold value compared with the control value and are shown as the mean ± SD for 3 separate experiments. (C) Western blot analysis for the phosphorylation of NF‐κB p65. Hs68 cells were pre‐incubated for 1 hour with 20 μM curcumin and were then treated with 3 ng/mL TNF‐α for 5 minutes. (D) Immunofluorescence staining for NF‐κB p65. Hs68 cells were pre‐incubated for 1 hour with 20 μM curcumin and were then treated with 3 ng/mL TNF‐α for 30 minutes. Representative results from 3 separate experiments are shown. (E) Cells were co‐incubated for 1 hour with 0–20 μM Bay11–7082 (a NF‐κB inhibitor) and then with 3 ng/mL TNF‐α for 23 hours. Cell lysates were prepared and assayed for MMP‐9 by Western blot. The data are expressed as mean ± SD for 3 separate experiments. *P < .05 compared with the untreated cells. †P < .05 compared with the TNF‐α‐treated cells

Article Snippet: Hs68 cells were pre‐treated with different concentrations of curcumin (0‐2.5 μM) and an MMP‐9 inhibitor (CAS 1177749584;5 μM; Santa Cruz Biotechnology) for 1 hour.

Techniques: Expressing, Incubation, Immunofluorescence, Staining, Western Blot

Journal: International Wound Journal

Article Title: Curcumin accelerates cutaneous wound healing via multiple biological actions: The involvement of TNF‐α, MMP‐9, α‐SMA, and collagen

doi: 10.1111/iwj.12904

Figure Lengend Snippet: Curcumin increases myofibroblast differentiation via the inhibition of NF‐κB expression. (A) Immunohistochemical staining for α‐SMA expression in control and curcumin‐treated wounds at the determined time. The marked area in the upper panel was shown in the lower panel at a higher magnification. α‐SMA‐positive myofibroblasts were increased in the curcumin‐treated group than the control group at postoperative day 7 and 12. The arrowheads indicate myofibroblasts. Scale bar: upper = 1 mm; lower = 100 μm. (B) Hs68 cells were incubated for 1 hour with a different concentration of curcumin; then, the cells were incubated with 3 ng/mL of TNF‐α for 23 hours. α‐SMA expression was analysed by immunofluorescence staining. Scale bar = 50 μm. (C) Hs68 cells were incubated with the indicated concentration of curcumin for 1 hour and then with 3 ng/mL TNF‐α for 23 hours in the continued presence of curcumin; α‐SMA protein in cell lysates was then measured by Western blot. GAPDH was used as the loading control. (D) Cells were co‐incubated for 1 hour with 0–20 μM Bay11–7082 (a NF‐κ B inhibitor) and then with 3 ng/mL TNF‐α for 23 hours. Cell lysates were prepared and assayed for α‐SMA by Western blot. The data are expressed as a fold value compared with the control value and are shown as the mean ± SD for 3 separate experiments. *P < .05 compared with the untreated cells. †P < .05 compared with the TNF‐α‐treated cells

Article Snippet: Hs68 cells were pre‐treated with different concentrations of curcumin (0‐2.5 μM) and an MMP‐9 inhibitor (CAS 1177749584;5 μM; Santa Cruz Biotechnology) for 1 hour.

Techniques: Inhibition, Expressing, Immunohistochemical staining, Staining, Incubation, Concentration Assay, Immunofluorescence, Western Blot

Journal: International Wound Journal

Article Title: Curcumin accelerates cutaneous wound healing via multiple biological actions: The involvement of TNF‐α, MMP‐9, α‐SMA, and collagen

doi: 10.1111/iwj.12904

Figure Lengend Snippet: Curcumin increases collagen production in wound and in TNF‐α‐treated fibroblasts. (A) The sections with Masson's trichrome staining of wound were scanned with an Aperio CS2 digital pathology scanner. The dotted marked area in the upper panel was shown in the lower panel at higher magnification. Scale bar: upper = 3 mm; lower = 1 mm. (B) The scoring system was used to evaluate collagen deposition (blue colour) by Masson's trichrome staining, using a 5‐point visual scoring scale. The collagen deposition score (grade 5) in the curcumin‐treated group was significantly greater than the control. *P < .05 compared with the control group at the determined time. (C) The level of collagen protein was measured by Western blot. Hs68 cells were incubated with the indicated concentrations of curcumin for 1 hour and then with 3 ng/mL TNF‐α for 23 hours in the continued presence of curcumin; collagen protein in cell lysates was then measured by Western blot. GAPDH was used as the loading control. The data are expressed as a fold value compared with the control value and are shown as the mean ± SD for 3 separate experiments. *P < .05 compared with the untreated cells. †P < .05 compared with the TNF‐α‐treated cells. (D) The total amount of collagen was measured by Sircol collagen assay. Hs68 cells were incubated with the MMP‐9 inhibitor (5 μM) and with the indicated concentrations of curcumin for 1 hour and then with 3 ng/mL TNF‐α for 23 hours. The supernatant was collected, and the level of collagen was measured by Sircol collagen assay. *P < .05 compared with the TNF‐α‐treated cells. †P < .05 compared with the TNF‐α+MMP9 inhibitor‐treated cells. ǂP < .05 compared with the TNF‐α+curcumin‐treated cells

Article Snippet: Hs68 cells were pre‐treated with different concentrations of curcumin (0‐2.5 μM) and an MMP‐9 inhibitor (CAS 1177749584;5 μM; Santa Cruz Biotechnology) for 1 hour.

Techniques: Staining, Western Blot, Incubation, Sircol Collagen Assay

Journal: Oncotarget

Article Title: Tumor suppressor REIC/DKK-3 and co-chaperone SGTA: Their interaction and roles in the androgen sensitivity

doi: 10.18632/oncotarget.6488

Figure Lengend Snippet: A. The yeast two-hybrid analysis was conducted using pPC86 (AD)/full-length human SGTA (derived from a normal heart cDNA library) and pDBLeu (BD)/full-length human REIC/DKK-3 plasmids. The blue colonies indicate those with an interaction between the two proteins. B. For the pull-down (PD) assay, the full-length cDNA of human REIC/DKK-3 and SGTA was cloned into the pFN21A and pMACS Kk.HA-C plasmids, respectively. Cell lysates from Halo-tagged REIC/DKK-3- and/or HA-tagged SGTA-transfected 293T cells were analyzed. The sample pulled down using Halo-tagged REIC/DKK-3 was analyzed by Western blotting (WB) using anti-HA antibody. C. REIC/DKK-3 and SGTA protein expression in 293T, PC3 and Hs68 cells was analyzed by Western blotting. Coomassie Brilliant Blue (CBB) staining of the membrane is shown as a loading control. D. The co-localization of REIC/DKK-3 and SGTA was examined by double immunofluorescence staining and observed by fluorescence microscopy. The images in green and red show the intracellular localization of REIC/DKK-3 and SGTA, respectively. The areas of overlap between REIC/DKK-3 and SGTA are shown in yellow in the merged image.

Article Snippet: The normal human foreskin fibroblast cell line Hs68 was provided by JCRB Cell Bank (Osaka, Japan).

Techniques: Derivative Assay, cDNA Library Assay, Clone Assay, Transfection, Western Blot, Expressing, Staining, Membrane, Control, Double Immunofluorescence Staining, Fluorescence, Microscopy

Journal: Stem Cell Research & Therapy

Article Title: Adipose-derived stem cell spheroid-laden microbial transglutaminase cross-linked gelatin hydrogel for treating diabetic periodontal wounds and craniofacial defects

doi: 10.1186/s13287-023-03238-2

Figure Lengend Snippet: In vitro characterization of ADSCs and ADsp-mTG. A Pluripotent gene expression in ADSCs after 24 h of seeding. B The trilineage differentiation of ADSCs by Oil Red O (adipogenesis), Alizarin Red (osteogenesis), and Alcian Blue (chondrogenesis) at day 14. Scale bar: 100 µm. C The spreading of ADSCs from spheroids embedded in mTG. D The trilineage differentiation of ADsp in mTG at day 14. Scale bar: 100 µm. There is no downstream processing or averaging to adjust the resolution of the microscopic images. (Significant difference compared to Hs68: * p < 0.05, ** p < 0.01, *** p < 0.001)

Article Snippet: Hs68 (cell line) , Bioresource Collection and Research Center, Hsinchu, Taiwan.

Techniques: In Vitro, Gene Expression

Journal: Stem Cell Research & Therapy

Article Title: Adipose-derived stem cell spheroid-laden microbial transglutaminase cross-linked gelatin hydrogel for treating diabetic periodontal wounds and craniofacial defects

doi: 10.1186/s13287-023-03238-2

Figure Lengend Snippet:

Article Snippet: Hs68 (cell line) , Bioresource Collection and Research Center, Hsinchu, Taiwan.

Techniques: Isolation, cDNA Synthesis, Staining, Polymer

Journal: Pharmaceutics

Article Title: Comprehensive Analysis of the Safety Profile of a Single-Stranded RNA Nano-Structure Adjuvant

doi: 10.3390/pharmaceutics11090464

Figure Lengend Snippet: Dose-dependent cell viabilities of Hs68 cell line treated with the ssRNA nano-structure adjuvant, using MTT assays. Relative viabilities of Hs68 cells were compared to negative control (0 concentration of ssRNA nano-structure adjuvant) from 24 h to 72 h, based on the ssRNA concentration (20 and 200 μg). Poly I:C (20 and 200 μg) was used as a positive control. Unlike poly I:C, the ssRNA did not affect cell viability in Hs68 cells. The data were normalized to 100%. The data shown are expressed as the mean ± SD.

Article Snippet: HS68 cells, derived from human foreskin fibroblast were obtained from Korean Cell Line Bank (Seoul, Korea).

Techniques: Adjuvant, Negative Control, Concentration Assay, Positive Control

Journal: PLoS Computational Biology

Article Title: A comprehensive overview and evaluation of circular RNA detection tools

doi: 10.1371/journal.pcbi.1005420

Figure Lengend Snippet: Comparison of circRNA candidates detected with and without RNase R treatment.

Article Snippet: Hs68_RNaseR− , rRNA (−) , Illumina PE100 , 202,521,855 , SRR444975.

Techniques:

Journal: PLoS Computational Biology

Article Title: A comprehensive overview and evaluation of circular RNA detection tools

doi: 10.1371/journal.pcbi.1005420

Figure Lengend Snippet: Coverage between circRNA detection methods on (a) HeLa and (b) Hs68 RNase R–treated data. For a pair of methods (i, j), the number of candidates detected by each method and the common candidates between them are calculated, then the proportion of common candidates for each method can be further computed and depicted. Cells within the same column reflect proportions of candidates detected by a specific method (column name) covered by other methods (row names) while cells within the same row show the proportions of candidates detected by other methods (column names) covered by a specific method (row name). CE, CIRCexplorer; CF, circRNA_finder; circRNA, circular RNA; FC, find_circ; MS, MapSplice; SG, Segemehl; NCLS, NCLScan; PF, PTESFinder; RNase R, exonuclease that digests linear RNAs but preserves circRNAs; UB, UROBORUS.

Article Snippet: Hs68_RNaseR− , rRNA (−) , Illumina PE100 , 202,521,855 , SRR444975.

Techniques:

Journal: PLoS Computational Biology

Article Title: A comprehensive overview and evaluation of circular RNA detection tools

doi: 10.1371/journal.pcbi.1005420

Figure Lengend Snippet: On (a) HeLa and (b) Hs68 RNase R–treated samples, common circRNA candidates detected by all the methods (659 and 903) and deposited in CircBase (608 and 724) were extracted, then candidates of which the spliced length is smaller than insert size of 500 base pairs (bp) and 400 bp, respectively, were further excluded (212 and 323). The numbers in parentheses above are candidates left after each filtering step. The number of supporting reads per candidate (log2 transformed) reported by each method was used in the cluster analysis. Each column represents a circRNA candidate and each row represents detection result of a specific method. The dendrogram was constructed via the average linkage hierarchical clustering approach, with intermediate Euclidean distance method chosen. (c) Backspliced junction reads recovery on positive dataset. After removing small-size candidates (smaller than insert size of 350 bp), proportion of backspliced junction reads recovered per candidate for the remnant common candidates was calculated for each method, and the results were used to depict the boxplot. circRNA, circular RNA; RNase R, exonuclease that digests linear RNAs but preserves circRNAs.

Article Snippet: Hs68_RNaseR− , rRNA (−) , Illumina PE100 , 202,521,855 , SRR444975.

Techniques: Transformation Assay, Construct

Journal: PLoS Computational Biology

Article Title: A comprehensive overview and evaluation of circular RNA detection tools

doi: 10.1371/journal.pcbi.1005420

Figure Lengend Snippet: A total of 282 experimentally verified circRNAs were manually compiled from 17 studies, and the number of circRNAs rediscovered by each method on HeLa and Hs68 RNase R–treated samples was computed. CircRNAs with ≥1 and ≥2 supporting reads are shown in light blue and deep blue color, respectively. GE1: candidates with greater than or equal to 1 supporting reads; GE2: candidates with greater than or equal to 2 supporting reads. circRNA, circular RNA; RNase R, exonuclease that digests linear RNAs but preserves circRNAs.

Article Snippet: Hs68_RNaseR− , rRNA (−) , Illumina PE100 , 202,521,855 , SRR444975.

Techniques:

Journal: PLoS Computational Biology

Article Title: A comprehensive overview and evaluation of circular RNA detection tools

doi: 10.1371/journal.pcbi.1005420

Figure Lengend Snippet: (a) Computational cost for each method on metrics of runtime, (b) memory consumption, and (c) physical disk space usage. While HeLa_RNaseR+ and HeLa_RNaseR− datasets are moderately sized RNA-Seq datasets, Hs68_RNaseR+ and Hs68_RNaseR− are examples of datasets with deep sequencing depth. Note: The analyses were run on an Ubuntu 10.04 server with two Intel® Xeon® E5530 Central Processing Units and 102 gigabytes of RAM. The running time presented was based on at most 3 threads allocated for each tool.

Article Snippet: Hs68_RNaseR− , rRNA (−) , Illumina PE100 , 202,521,855 , SRR444975.

Techniques: RNA Sequencing Assay, Sequencing

Journal: PLoS Computational Biology

Article Title: A comprehensive overview and evaluation of circular RNA detection tools

doi: 10.1371/journal.pcbi.1005420

Figure Lengend Snippet: Summary of the datasets used in this study.

Article Snippet: Hs68_RNaseR− , rRNA (−) , Illumina PE100 , 202,521,855 , SRR444975.

Techniques: Sequencing