Transanal Endoscopic Microsurgery

Creating the CN map of the retina requires digitizing each tissue section and registering it to its neighbors. Creating a volume of this scale is a significant undertaking: The CN map for the rabbit inner plexiform layer in the visual streak requires a volume delimited by a canonical field 250 μm in diameter × 30 μm high: roughly 1.47 × 10⁶ μm³. A cylindrical volume is a more efficient capture object than rhomboidal prisms that will have extremities clipped out as sections are rotated during registration. While the issue is irrelevant at small volumes [18 (link)], it tremendously impacts beam time at canonical scales. In practice, at a magnification of 5,000× on the JEOL JEM-1400, we capture 950–1,100 images or tiles/section and ≈333 sections at 70–90-nm thickness. Storage of unprocessed 16-bit images requires 10.4 terabytes. With a time of capture at roughly 30 s/frame, this requires some 70–100 calendar days on a single TEM, which argues for automated capture scripts and efficient capture geometries. To ensure the images can be positioned properly in the total mosaic, each image has 15% area overlap with its neighbors. With some of the software tools developed below, it is also evident that such tasks can be parallelized across microscopes and users.
We capture ssTEM data using SerialEM software developed by D. Mastronarde at the University of Colorado at Boulder [72 (link)]. Though originally developed for TEM tomography, SerialEM is ideal for large-scale mosaicking. The most recent build version of SerialEM allows definition of multiple circular or polygonal regions of interest on a grid and automates stage drive and image capture within the regions of interest on the JEOL JEM 1400 TEM (and other recent TEMs as well such as FEI Tecnais) and, critically, stores stage position metadata for each tile. This greatly reduces the computational cost of the initial positioning of mosaic tiles from O(n²) to O(n). The program includes a scripting capability that provides the flexibility needed to optimize the acquisition strategy, for example, by focusing only on an appropriately spaced subset of the image tiles. While automated capture is ideal for the microscope's Gatan Ultrascan 4000 (4K × 4K) camera, it can also serve on a smaller scale with film capture.

A Disease Activity Index (DAI) was determined daily in all mice by scoring for body weight, hemocult reactivity or presence of gross blood and stool consistency during the week of DSS induction, as detailed in [25 (link)]. Stool was collected from individual mice and tested for the presence of blood using Hemoccult II slides (Beckman Coulter Inc., Brea, CA, USA). Briefly, the following parameters were used for calculation: (a) body weight loss (score 0 = 0%, score 1 = 1–5%, score 2 = 6–10%, score 3 = 11–15%); (b) stool consistency (score 0 = normal, score 1 = soft but still formed, score 2 = very soft/loose stool, score 3 = diarrhoea/watery stool); and (c) blood in stool (score 0 = negative hemoccult, score 1 = positive hemoccult, score 2 = Blood traces in stool visible, score 3 = rectal bleeding). DAI was determined by combining the scores from these three categories. Body weights were measured for each animal throughout the experiments and expressed as percent weight loss to the weight immediately before DSS treatment. Fecal samples were collected and stored at −80 °C on day 14 for metabolite analysis.
After sacrificing the mice, the colons were removed from the caecum to the anus following the method of Perera et al. [26 (link)]. The length of the colons from the ileocaecal junction to the rectum were recorded. The colon was subsequently opened along its longitudinal axis and the luminal (mucosal) contents were removed using sterilised 200 μL pipette tips prior to weighing the organ. The length and weight of colon and spleen were documented. Spleen weight, colon length, and colon weight/body weight ratio were calculated as macroscopic markers of inflammation. The mucosal and cecal contents were collected for metabolite profiling and stored at −80 °C. The colon was bisected longitudinally, and one half was prepared using the Swiss roll technique [27 (link)] whereas the remaining colonic tissue was dissected out, segregated into proximal colon (PC) and distal colon (DC) and snap-frozen for molecular analyses. Swiss rolls underwent 24 h fixation in 10% (v/v) neutral-buffered formalin. Swiss rolls were subsequently transferred to 70% ethanol prior to progressive dehydration, clearing and infiltration with HistoPrep paraffin wax (Fisher Scientific, Philadelphia, PA, USA). Swiss rolls were then embedded in wax and 5 μm sections were cut using a rotary microtome. Sections were stained with haematoxylin and eosin (H and E; HD Scientific, Sydney, Australia). Slides stained with H and E (n = 8 per group) graded blindly for the severity of tissue damage at distal and proximal regions as described previously [28 ,29 (link)]. Briefly, frequency of distribution of inflammation graded 0-3, crypt architectural distortion and ulceration graded 0–5, tissue damage graded 0-3, inflammatory infiltrate graded 0–3, goblet cell loss graded 0–3, mucosal thickening (oedema) were graded 0–3. All images were captured on a Leica DM500 microscope using a Leica ICC50 W camera (Leica Microsys-tems, Wetzlar, Germany).

Ethnomedicinal use data of wild medicinal plants was gathered from the local informants using structured and semi-structured interview methodology with documentation of the information in questionnaire form during year-2019 (Fig 2). About 200 informants comprising of both male (120) and female (80) were involved in the study and during all trips one male and one female guide or translator was accompanied because rural and mountainous people use different languages and dialects, hence it was very important to use indigenous translator to know the real knowledge about indigenous flora from the native communities [49 –52 ]. The interviewees were farmers, house-women, midwives, herbalist and traditional phytotherapist (TPT) while it has been seen that most of the people were illiterate or had very basic education and only few had graduation level literacy; thus extracting diverse and maximum knowledge of TEMs from the indigenous communities. In the research trips not only traditional knowledge of TEMs was documented but also plant specimens were collected with guidance of local people describing their native names and tonic preparation methods were narrated in field notebook. For this study, interviewees were selected gender free manner and each collected plant was showed to “five or more” individuals and asked to tell their ethnomedicinal, ethnobotanical uses and occurrence place with population density of each species. In this procedure, if same data or information about the species was described by three or more than three persons (>60%) then it was declared “authentic” and included in the study for better reliability and further research analysis. However, the less information collected about certain plants does not mean that they are of less significance in TEMs, that might be due to reason that traditional ethnomedicinal knowledge (TEK) about wild plants is gradually disappearing in younger generations or population of the plants is becoming scarce day by day due to various threatening factors [53 (link), 54 (link)]. For proper authentication of the ethnomedicinal data, the botanical and local names with family of each plant were verified manually with herbarium specimens, taxonomic literature (hard and soft form), manuals, Flora of Pakistan and cross checked with online data from the plant list website or flora of Pakistan & litrature [55 –59 (link)]. All collected plants were brought under standardized voucher numbering system with labels and cross-referenced with field notebook (FNB) record to further validate their authenticity [60 (link)–62 (link)]. All gathered ethnomedicinal data of plants was presented in alphabetical order comprising of botanical names, common names, family, plant parts, preparation mode, administration method, diseases cured and other ethnobotanical uses promulgated in the study area. During the field surveys, plants specimens were collected properly (having flowers, fruit or both) and preserved according to the standard process for herbarium (MUH) [47 , 63 ]. The herbarium specimens were prepared according to protocol of previous researchers like Seshagirirao et al., (2016), Vitalini et al., (2013) and Ishtiaq et al., (2010a) [39 , 64 (link), 65 (link)] and deposited in the herbarium of the Department of Botany with proper voucher number allotment for future reference because they will assist taxonomy students and researchers to identify the required species for further collection from same and/or other areas of the study area and Azad Kashmir regions [66 (link), 67 (link)]. The collected plant specimens were properly identified using Flora of Pakistan data “www.eflora.com”, the plant list “www.theplantlist.org” and comparing with printed Flora book [68 (link), 69 (link)]. Whereas another website named “International Plant Name Index” with webpage “www.ipni.org” was used for cross checking of botanical and family names of the plants [70 (link)]. The collected plants were identified by Dr. Muhammad Ajaib; a taxonomist of the Department of Botany and all prepared herbarium vouchers bearing code “MUH-”, were kept in herbarium, Department of Botany Mirpur University of Science and Technology (MUST), Bhimber Campus, AJK, Pakistan for future reference.

After gating for CD4⁺ T cells using FlowJo, pre-processed data were considered for the analysis. All FlowSOM-based k-NN clustering was performed on the combined dataset to enable identification of small populations. For CD4⁺ TEMs, resulting nodes were meta-clustered with the indicated k-values (based on the elbow criterion) and annotated manually. FlowSOM k-NN clustering and two-dimensions UMAP projections were calculated using the CyTOF workflow package (v. 1.2)^{97 (link)}.

BMDMs seeded on glass coverslips at density 1 × 10⁶ cells per well in 24-well tissue culture were infected with F. tularensis FSC200, as described above, for 2, 12, and 24 h. Following infection, the cells were briefly washed in pre-warmed Sörensen’s buffer (0.1 M Na/K phosphate buffer, pH 7.2–7.4) and fixed in 2.5% paraformaldehyde with 0.25% glutaraldehyde (Thermo Fisher Scientific) in Sörensen’s buffer for 1 h at room temperature (RT). All subsequent steps were performed on ice. After several washes, free aldehyde groups were quenched with 0.02 M glycine in Sörensen’s buffer for 10 min. Samples were then dehydrated in a series of ethanol (Lach-Ner, Neratovice, Czech Republic) and embedded into LR White resin (Sigma-Aldrich). After polymerization for 72 h under UV light at 4 °C, 80 nm ultrathin sections were prepared using the Ultramicrotome Leica EM UC6 (Leica Microsystems, Wetzlar, Germany) equipped with a diamond knife (Diatome, Biel, Switzerland). The sections were mounted on formvar-coated 3.05 mm gilded copper slots (Agar scientific, Essex, UK), immunogold-labeled following a conventional protocol [29 (link)], and examined in an FEI Morgagni 268 transmission electron microscope (TEM) with Mega View III CCD camera (Olympus Soft Imaging Solutions, Münster, Germany) or in a Jeol JEM-1400 FLASH TEM equipped with 2kx2k Matataki CMOS camera. Both TEMs were operated at 80 kV. Antibodies used for immunolabeling were primary rabbit anti-FTT1368 (GapA) antibody at dilutions 1:25 and 1:100 (Apronex, Vestec, Czech Republic) and secondary goat anti-rabbit IgG (H + L) antibody coupled with 12 nm colloidal gold particles (Jackson ImmunoResearch Laboratories Inc., Baltimore Pike, West Grove, PA, USA; 111-205-144; dilution 1:40). Uninfected BMDMs were used as a control of specificity of the GapA antibody, and usual technical negative controls were performed with an omitted primary antibody. The density of immunolabeling in specified compartments of bacterial cells and host cells was calculated as a ratio between the number of gold nanoparticles in a specified region and its area in µm². The areas were measured in ELLIPSE Software, version 2.0.8.1 (ViDiTo, Kosice, Slovakia). The calculation of statistical significance was based on a comparison of labeling density values and variance in individual cells between analyzed variants.