Detection of peroxidase activity in trypsin-macerated tissues
Detection of peroxidase activity in trypsin-macerated tissues
Intact Hv_AEP2 were relaxed in 2% urethane/HM for 1 min, fixed in 4% PFA for 4 hours, and then washed several times in PBS. Macerates were prepared by pooling 20 heads and 20 feet, respectively, and subsequently incubated in 100 μl of 0.05% trypsin-EDTA for 45 min at 37°C. Next, 100 μl of 8% PFA was added, the cell suspension was incubated for 30 min at RT, and then 20 μl of Tween 80 was added, and the cell suspension was spread on slides and dried for 2 to 3 hours. All samples were processed immediately to detect peroxidase activity. The samples were washed in PBS-Tw for 5 min and incubated in Alexa Fluor 647 or 488 tyramide solution [1× tyramide reagent (Thermo Fisher Scientific, references: B40958 and B40953)] and 0.03% H2O2 in PBS-Tr (0.25% Triton X-100 in PBS) for 20 min. Next, the samples were briefly washed in PBS-Tw and kept in PBS-Tw for 15 min. After one quick wash in PBS, the samples were stained with DAPI (0.2 μg/ml; Roche) for 10 min, washed with Milli-Q water for three times for 1 min, and mounted in Mowiol.
Imaging
ISH images and photos of animals that were fixed after RNAi were taken using an Olympus SZX10 microscope with an Olympus DP73 camera and a Leica DM5500 B microscope with a Leica DFC9000 GT camera. Aggregates were imaged with the Olympus SZX10 microscope. Fluorescent images were collected with the Leica SP8 confocal microscope. Spinning disc confocal microscopy was performed using an Intelligent Imaging Innovations (3i) system that incorporates a Yokogawa CSU-W1 scanner unit and a Nikon Ti inverted microscope. It is equipped with a back-illuminated Prime 95B sCMOS camera (1200 pixels × 1200 pixels, Teledyne Photometrics) and four excitation lasers (3i): 405, 488, 561, and 640 nm. Objectives used were Plan Apochromat 4×/0.2 WD 15.7 (Nikon MRD00045), Plan Apochromat 40×/0.95 OFN25 DIC M/N2 WD 0.14 (Nikon MRD00400), Plan Fluor 60×/0.85 OFN25 DIC N2 (Nikon MRH00602), and Apochromat TIRF 100×/1.49 Oil WD 0.12 (Nikon MRD01991). Microscope was controlled, and images were acquired using SlideBook 6 software (3i). For peroxidase activity detected in macerated tissues, cells were imaged using spinning disc confocal microscopy with bright-field channel (single z slice) and far red channel corresponding to peroxidase detected with Alexa Fluor 647 tyramide. All images were acquired with the same time exposure, and minimal-maximum intensities were adjusted similarly 250 to 487, maximum projection, and then merged.
Statistical analysis
RNA-seq analysis
Total RNA was extracted from head and body column tissue of control and Zic4 RNAi animals 1 day after EP4. Twenty Hydra were used per condition, and RNA was extracted with E.Z.N.A. Total RNA Kit I (Omega), following the manufacturer’s instructions. The RNA quality control, library preparation using TruSeqHT Stranded mRNA (Illumina), and sequencing on the Illumina HiSeq 4000 System using the 100-bp single-end read protocol were performed at the iGE3 genomics platform of the University of Geneva. The quality control of the resulting reads was done with FastQC v.0.11.5. Mapping to the H. vulgaris genome [Jussy reference—(32)] and transcript quantification were performed with the Salmon v.1.1.0 software (70). As a prefiltering step, only the transcripts with at least a sum of reads equal or above to 50 inside a same biological condition were kept. Normalization and differential expression analysis were performed with the R/Bioconductor package DESeq2 v.1.26.0 (71). The P values of the differentially expressed genes were corrected for multiple testing error with a 5% false discovery rate (FDR) using the Benjamini-Hochberg correction. Transcripts with a fold change of >1.5 and adjusted P value of <0.05 were considered as differentially expressed.
For RNA-seq of tentacle samples, original and ectopic tentacles were collected 7 days after EP3 from control, Zic4, Sp5, and Zic4/Sp5(RNAi) animals (Hm-105). The RNA was extracted with the Single Cell RNA Purification Kit (Norgen), according to the supplier’s instructions. The resulting RNA quality was assessed using a bioanalyzer or TapeStation RNA HS kit, and concentration was determined using Qubit. cDNA amplification was performed using the SmartSeq2 approach as per the original protocol (72). Full-length cDNA was processed for Illumina sequencing using Tagmentation with an in-house purified Tn5 transposase (73). One nanogram of amplified cDNA was tagmented in TAPS-DMF buffer [10 mMN-[Tris(hydroxymethyl)methyl]-3-aminopropansulfonsäure, [(2-Hydroxy-1,1-bis(hydroxymethyl)ethyl)amino]-1-propansulfonsäure-TAPS (pH 8.5), 5 mM MgCl2, and 10% N,N′-dimethylformamide (DMF)] at 55°C for 7 min. Tn5 was then stripped using SDS (0.04% final concentration), and tagmented DNA was amplified using Phusion High-Fidelity DNA Polymerase. The Illumina SmartSeq2 libraries were then demultiplexed, and reads were aligned against the Hydra vulgaris National Center for Biotechnology Information (NCBI) genome guided by transcriptome annotation (NCBI Hydra vulgaris assembly Hydra_RP_1.0, NCBI Hydra vulgaris annotation release 102).
Libraries with sizes more than 2 SDs below the median library size were discarded from all subsequent analyses. The STAR-produced gene count tables for all samples were library-normalized to obtain counts per million (CPMs) after excluding from the size factor calculation of the top five percentiles of highly expressed genes.
Marker gene selection and PCA projections
The foot- and tentacle-specific markers were selected on the basis of the average expression profiles in the positional RNA-seq data (74). Genes with low expression were first filtered out of this dataset (average CPM ≤ 200 in the body part with the lowest expression), and the remaining values were normalized for each gene to the body position with its maximum expression. Then, genes with a value of 1 in tentacles or foot were only retained in the dataset. The specificity for foot/tentacles was determined as a difference of the weighted sum of the remaining body parts to the investigated position. For example, for calculating tentacle specificity, the following formula would be used: Tspecificity = wtent.CPMtent – (whead.CPMhead + wbody1.CPMbody1 + … + wfoot.CPMfoot), where w is the weight, assigned to each position. The weighting scheme was designed to penalize distant positions more, to avoid picking genes that have similarly high expression in both foot and tentacles. Thus, the position for which the specificity was calculated (tentacles or foot) would be assigned a weight of 9 and the immediately neighboring position a weight of 2, with a +1 increment in the weight for every further neighboring position. Last, genes with a specificity score of ≥5 were retained and considered marker genes for either position.
For the common subspace projection of the positional RNA-seq dataset and dissected tentacles from RNAi animals, the expression values were first log2-transformed after smoothing (fixed pseudocount of 8) to shrink the effect sizes of lowly expressed genes. For dimensionality reduction purposes only, genes with at least a twofold change between the top and bottom fifth percentiles of gene expression and a maximum CPM expression level of >5 in the body segment samples were used. Subsequently, we obtained a unique eigenvector basis by applying PCA on the animal segment data (base R function prcomp with parameters center = TRUE, scale = FALSE). Last, the data from the dissected tissues of RNAi animals were projected back to the eigenvector basis defined from the animal segment data. To select marker genes for the epithelial and interstitial lineages, and generate the respective PCA spaces, we relied on previously published single-cell RNA-seq data (38). Specifically, we used the cluster assignment and associated gene cluster marker analysis (table S7 in the above study) and assigned as tissue origin markers genes satisfying the following criteria: an average log fold change in a cluster of interstitial/epithelial identity of more than 1.5, more than 75% within-cluster positive cells for the gene, and a difference of more than 35% in terms of difference of within cluster and out of cluster percentages of positive cells for the gene.
Acknowledgments
We thank all members of the Tsiairis and Galliot laboratories for discussions, D. Duboule for comments on the manuscript, the iGE3 Genomic Platform for RNA-seq library preparation and sequencing, the FMI Genomics facility and S. Smallwood for RNA sequencing experiments, the FMI Imaging facility for support with microscopy, I. Katic for Hydra electroporations, D. Colevret for technical assistance generating the transgenic cell cycle sensor line, and S. Vianello for the help with illustrations. We also thank the two anonymous reviewers who provided constructive comments on this work.
Funding: Research in the Galliot laboratory was supported by Swiss National Science Foundation grants 31003_169930 and 310030_189122; P.G.L.S. was supported by a Swiss Government Excellence Scholarships for Foreign Scholars. Research in the Tsiairis laboratory was supported by the Novartis Foundation; Y.L.C. was supported by the EUR G.E.N.E. (reference no. ANR-17-EURE-0013).
Author contributions: Conceptualization: M.C.V., J.F., S.V., C.D.T and B.G. Methodology: M.C.V., J.F., B.G., and C.D.T. Investigation: M.C.V., J.F., W.C.B., C.P., P.G.L.S., L.B., C.N., Y.L.C., C.D.-V., and P.P. Visualization: M.C.V., J.F., W.C.B., P.G.L.S., L.B., B.G., and C.D.T. Funding acquisition: B.G. and C.D.T. Supervision: B.G. and C.D.T. Writing—original draft: M.C.V. Writing—review and editing: M.C.V., J.F., W.C.B., P.G.L.S., S.V., C.D.T and B.G.
Competing interests: The authors declare that they have no competing interests.
Data and materials availability: The RNA-seq data have been deposited in the GEO database under the accession codes GSE190110 and GSE191177. All other raw data can be found in dataset S6.
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