tessandier.fr

On to the radio

nicolas.tessandier (/.at./) umontpellier.fr (Nicolas Tessandier) — Sat, 22 Mar 2025 03:51:34 +0100

Radio Divergence

Radio Divergence is a local radio affiliated to the Montpellier University.

I had the pleasure to be invited to talk about our recent Plos Biology (2025) publication, co-led with Baptiste Elie and conceived by Samuel Alizon. The story started in 2016, so it’s impossible to thank everyone involved here !

The paper itself is available here.

You can find the original interview on radio divergence website.

You can also listen to the interview straight here !

Unfortunately the interview is only available in french, but feel free to contact me if you want to chat about it english !

More news soon-ish, hopefully !

We are hiring !

nicolas.tessandier (/.at./) umontpellier.fr (Nicolas Tessandier) — Wed, 06 Nov 2024 03:51:34 +0100

We currently have two open positions to join the team, funded for 2 years:

Post-doc position for 2 years in Montpellier (France)

Postdoctoral researcher – Microfluidic and clinical approaches to study the impact of menstrual protection on the vaginal environment

You can find the offer here: french or english.

Research engineer for 2 years in Montpellier (France)

Microfluidic Research Engineer – Implementation of a vagina-on-a-chip system vaginal environment

You can find the offer here: french or english

If you want to apply, please drop me an email at with a motivation letter and a CV.

Unsupervised flow cytometry with R

nicolas.tessandier (/.at./) umontpellier.fr (Nicolas Tessandier) — Wed, 06 Nov 2024 02:41:16 +0100

Here is some code you might find useful for unsupervised flow cytometry analysis with R

Script used to generate figures and tables for the paper “Viral and immune dynamics of HPV genital infections” currently in review (2024) in Plos Biology

Original contributors:

Nicolas Tessandier:
Baptiste Elie
Samuel Alizon

For any questions, please contact me at

Associated data can be found at: https://doi.org/10.57745/KJGOYZ

#### Script used to generate figures and tables for the paper:
#"Viral and immune dynamics of HPV genital infections" currently in review (2024) in Plos Biology

# Original contributors:
# Nicolas Tessandier
# Baptiste Elie
# Samuel Alizon
# For any questions, please contact Nicolas Tessandier (email above or at https://tessandier.fr/contact/)
# Associated data can be found at: https://doi.org/10.57745/KJGOYZ
#####################
# NO COMMERCIAL USE #
# CC BY-NC-SA 4.0   #
#####################


##### Original script
suppressPackageStartupMessages(library('tidyverse'))
suppressPackageStartupMessages(library('magrittr'))
suppressPackageStartupMessages(library('flowCore'))
suppressPackageStartupMessages(library('FlowSOM'))
suppressPackageStartupMessages(library('CATALYST'))
suppressPackageStartupMessages(library('diffcyt'))
suppressPackageStartupMessages(library('cytofWorkflow'))
suppressPackageStartupMessages(library('ComplexHeatmap'))
suppressPackageStartupMessages(library('RColorBrewer'))
suppressPackageStartupMessages(library('ggpubr'))
suppressPackageStartupMessages(library('xtable'))
suppressPackageStartupMessages(library('viridis'))
suppressPackageStartupMessages(library('ggridges'))

# Load metadata
rm(list=ls())
experiment_info <- read_csv("./tables/experiment_info_PlosBio_R2.csv")
panel <- read_csv("./tables/panel_info_PlosBio_R2.csv")

# Load files
fcsfiles <- list.files(path = "./fcs", pattern = "\\.fcs$", full.names = TRUE)

# Load files in flowSet
d_flowSet <- read.flowSet(fcsfiles, transformation = FALSE, truncate_max_range = FALSE)

# Load fcs into sce

sce <- prepData(d_flowSet, panel, experiment_info,features = panel$fcs_colname, FACS = TRUE,
    md_cols = list(file = "file_name", id = "sample_id",
        factors = c("condition", "vaccine", "patient_id")))

# Set seed
set.seed(42)

# Cluster all cells
sce <- cluster(sce, features = rownames(sce)[c(1, 3, 6:7, 10, 13:15)],
xdim = 10, ydim = 10, maxK = 20, seed = 42, verbose = TRUE)

# Expression check (Figure just used to check CD45neg clusters,
# before restricting the dataset to CD45+ cells, not used in the paper)
pdf(paste0("./output/Figure0.pdf"), width = 16, height = 10)
u <- plotClusterHeatmap(sce,
hm2 = NULL, k = "meta20", m = NULL,
cluster_anno = TRUE, draw_freqs = TRUE)
plot(u)
dev.off()

# Remove CD45 negative cells

merging_manual_all_cells <- tibble(original_cluster = c(1:20),
new_cluster = c(
"Non-Leukocytes" ,# 1
"Non-Leukocytes" ,# 2
"Non-Leukocytes" ,# 3
"Non-Leukocytes" ,# 4
"Non-Leukocytes" ,# 5
"Non-Leukocytes" ,# 6
"Non-Leukocytes" ,# 7
"CD16+ leukocytes" ,# 8
"CD45low cells" ,# 9
"CD16+ granulocytes" ,# 10
"CD16+ granulocytes" ,# 11
"CD45low cells" ,# 12
"CD16+ granulocytes" ,# 13
"CD45+ CD3- CD16- cells (1)" ,# 14
"TCRgd cells" ,# 15
"CD16+ granulocytes" ,# 16
"CD16+ granulocytes" ,# 17
"CD45+ CD3- CD16- cells (2)" ,# 18
"CD4 T cells" ,# 19
"CD8 T cells" # 20
))

merging_manual_all_cells$new_cluster <- factor(merging_manual_all_cells$new_cluster,
levels = c(
"Non-Leukocytes" ,# 1
"CD16+ leukocytes" ,# 8
"CD45low cells" ,# 9
"CD16+ granulocytes" ,# 10
"CD45+ CD3- CD16- cells (1)" ,# 14
"TCRgd cells" ,# 15
"CD45+ CD3- CD16- cells (2)" ,# 18
"CD4 T cells" ,# 19
"CD8 T cells" # 20
))
merging_manual_all_cells

### apply manual merging ###
sce <- mergeClusters(sce, k = "meta20", table = merging_manual_all_cells, id = "merging1", overwrite=TRUE)

sub <- filterSCE(sce, !cluster_id %in% c(1, 2, 3, 4, 5, 6, 7) , k = "meta20")

# Re-clustering the CD45-restricted subset
set.seed(42)
sub <- cluster(sub, features = rownames(sce)[c(1, 3, 6:7, 10, 13:15)],
xdim = 10, ydim = 10, maxK = 20, seed = 42)

#
# Annotating clusters check (Figure just used to check manual annotation of clusters, not used in the paper)
pdf(paste0("./output/Figure02.pdf"), width = 16, height = 10)
u <- plotClusterHeatmap(sub,
hm2 = NULL, k = "meta20", m = NULL,
cluster_anno = TRUE, draw_freqs = TRUE)
plot(u)
dev.off()

merging_manual_leuko_only <- tibble(original_cluster = c(1:20),
new_cluster = c(
"I (CD16+ granulocytes)" , # 1
"I (CD16+ granulocytes)" , # 2
"III (CD16+ leukocytes)" , # 3
"II (CD16+ granulocytes)" , # 4
"I (CD16+ granulocytes)" , # 5
"XI (CD8 T cells)" , # 6
"VII (CD45low cells)" , # 7
"II (CD16+ granulocytes)" , # 8
"VIII (CD45low cells)" , # 9
"IX (TCRgd cells)" , # 10
"IV (CD16+ leukocytes)" , # 11
"I (CD16+ granulocytes)" , # 12
"IX (TCRgd cells)" , # 13
"V (CD45+ CD16- CD3- cells)" , # 14
"VIII (CD45low cells)" , # 15
"VIII (CD45low cells)" , # 16
"X (CD4 T cells)" , # 17
"V (CD45+ CD16- CD3- cells)" , # 18
"VI (CD45+ CD16- CD3- cells)" , # 19
"II (CD16+ granulocytes)" # 20
))

merging_manual_leuko_only$new_cluster <- factor(merging_manual_leuko_only$new_cluster,
levels = c(
"I (CD16+ granulocytes)" , # 1
"II (CD16+ granulocytes)" , # 2
"III (CD16+ leukocytes)" , # 3
"IV (CD16+ leukocytes)" , # 8
"V (CD45+ CD16- CD3- cells)" , # 14
"VI (CD45+ CD16- CD3- cells)" , # 7
"VII (CD45low cells)" , # 9
"VIII (CD45low cells)" , # 15
"IX (TCRgd cells)" , # 13
"X (CD4 T cells)" , # 17
"XI (CD8 T cells)"  # 6
))
merging_manual_leuko_only

### Merging for dataset restricted to leukocytes
sub <- mergeClusters(sub, k = "meta20", table = merging_manual_leuko_only, id = "merging1", overwrite=TRUE)

# Color palette for all the FCM figures
q11 <- CATALYST:::.cluster_cols[c( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)]

### UMAP
set.seed(42)
sub <- runDR(sub, "UMAP", cells = 150, features = rownames(sub)[c(1,3,7,6,14,13,15,10,8,11)])

## UMAP by condition
x <- plotDR(sub, "UMAP", color_by = "merging1", k_pal =  q11) +
facet_wrap("condition", labeller = labeller(vs = label_both, am = label_value) ) +
theme(legend.text=element_text(size=13),legend.position = "bottom") +
guides(colour = guide_legend(nrow =3,override.aes = list(size=3))) +
theme(strip.text.x = element_text(size = 20, face = "bold"),
strip.text.y = element_text(size = 20,face = "bold")) +
theme(legend.title=element_blank())

### Differential Abundance analysis ###
fdr_cutoff <- 0.1

ei <- metadata(sub)$experiment_info
da_formula <- createFormula(ei, cols_fixed = c("condition", "vaccine"), cols_random = "patient_id")
ds_design <- createDesignMatrix(ei, cols_design = c("condition", "vaccine"))
contrast.focal <- createContrast(c(0, 1, 0))
contrast.vaccine <- createContrast(c(0, 0, 1))

# Define the variables
da_res1_leuko_only.edgeR.focal <- diffcyt(
       sub,
       experiment_info = experiment_info,
       marker_info = panel,
       design = ds_design,
       formula = da_formula,
       contrast = contrast.focal,
       analysis_type = c("DA"),
       method_DA = c("diffcyt-DA-edgeR"),
       clustering_to_use = "merging1",
       plot = TRUE,
       path = "./output",
       verbose = TRUE
     )

da_res1_leuko_only.edgeR.vaccine <- diffcyt(
       sub,
       experiment_info = experiment_info,
       marker_info = panel,
       design = ds_design,
       formula = da_formula,
       contrast = contrast.vaccine,
       analysis_type = c("DA"),
       method_DA = c("diffcyt-DA-edgeR"),
       clustering_to_use = "merging1",
       plot = TRUE,
       path = "./output",
       verbose = TRUE
     )

plasma_pal <- c(viridis::viridis(n = 10, alpha = 0.6))
da_table1 <- as_tibble(topTable(da_res1_leuko_only.edgeR.focal, all = TRUE,
show_props = TRUE, format_vals = TRUE, digits = 3))

# Figure 2b
rowData(da_res1_leuko_only.edgeR.focal$res) %>% as_tibble() %>%
dplyr::select(cluster_id, logFC, p_val, p_adj) %>% mutate(FoldChange = 2^(logFC)) %>%
dplyr::select(-c(logFC)) %>% arrange(FoldChange, p_adj) %>% relocate(FoldChange, .before = p_val) -> res.edgeR

da_table1 %>%
	dplyr::select(-c(p_val)) %>%
	left_join(res.edgeR, by = c("cluster_id")) %>%
	dplyr::select(!c(p_adj.x, p_val)) %>%
	pivot_longer(!c(cluster_id, p_adj.y, FoldChange), names_to = "sample", values_to = "value") %>%
	mutate(sample_id = str_sub(sample, 7, 14)) %>%
	group_by(sample_id) %>%
	left_join(experiment_info) %>%
 ggplot(aes(fill= fct_rev(condition), y=value, x=cluster_id, label = FoldChange)) +
    geom_boxplot(outlier.shape = NA, reverse=TRUE) +
    scale_fill_manual( values = plasma_pal[c(10,1)], name = "") +
    scale_color_manual(values = plasma_pal[c(10,1)], name = "") +
    geom_point(colour = "black",position=position_jitterdodge(jitter.width = 0.15), alpha=0.5, size = 0.5) +
    #geom_text(aes(label = paste0("FC: ",round(FoldChange, 2),
    "\np.adj = ", round(p_adj.y, 4)), y=97, x = cluster_id) ) +
coord_flip() +
guides(fill = guide_legend(reverse = TRUE)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 0, hjust = 1, vjust = 1)) +
theme(legend.text=element_text(size=13, face = "bold"),legend.position = "bottom") +
theme(axis.text.x = element_text(face = "bold", size = 13)) +
theme(axis.text.y = element_text(face = "bold", size = 13))  +
scale_x_discrete(labels= c("1","2","3","4","5","6","7","8","9","10","11")) +
    xlab("") +
    ylim(c(0, 105)) +
    ylab("Frequency (%)") -> fig.2b

# Table for figure 2C
rowData(da_res1_leuko_only.edgeR.focal$res) %>% as_tibble() %>%
dplyr::select(cluster_id, logFC, p_val, p_adj) %>% mutate(FoldChange = 2^(logFC))
%>% dplyr::select(-c(logFC)) %>% arrange(FoldChange, p_adj)
%>% relocate(FoldChange, .before = p_val) %>% mutate(across(where(is.numeric), round, 3))


# Empty plot for figure 2C, the grid is generated manually through inkscape
# (and a lot of the figure is adapted that way, the legend of the UMAP for figure 2A, the Y axis for figure 2B)
bb <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_blank() + theme_void()
line2 <- ggarrange(x, widths = c(1),nrow = 1, labels =  c("A"),
font.label = list(size = 18, color = "black", face = "bold", family = NULL))
line3 <- ggarrange(fig.2b,bb, widths = c(2, 1),nrow = 1,
labels =  c("B", "C"),font.label = list(size = 18, color = "black", face = "bold", family = NULL))
figure2 <- ggarrange(line2, line3, ncol = 1)
ggsave(paste0("output/figure1_PlosBio_Tessandier_Elie.pdf"), plot = figure2,
       width = 15,
       height = 15,
       limitsize = FALSE,
       units = "in",
       dpi = 300)

# save FCM environement generated with figure1_PlosBio_Tessandier_Elie.r
save(file = "./RData/FCM_analysis_Plos_Bio_Tessandier_Elie.RData")

Contact

nicolas.tessandier (/.at./) umontpellier.fr (Nicolas Tessandier) — Tue, 27 Aug 2024 16:39:33 +0200

Interested in joining the team?

We always welcome applications from students and researchers, for MSc internships, PhD or post-docs !

We currently have two open positions (engineer, IE or IR) and post-doc. The thematic will cover the ExposUM Fellowship:

Combining a vagina-on-a-chip model and clinical investigation to evaluate the effect of chemicals from menstrual protections on the vaginal environment

Ideal applicants would have skills in cell culture, organoid culture, micro-fluidics system and/or bio-informatics (Metagenomics, RNASeq) or bio-statistics (longitudinal models, Bayesian modelling, conceptual modelling).

Detailed offers will be published soon-ish but feel free to get in touch whenever !

If you want to get in touch, please drop me an email at

Publications

nicolas.tessandier (/.at./) umontpellier.fr (Nicolas Tessandier) — Mon, 26 Aug 2024 17:34:53 +0200

Orcid

You can find my (usually more up to date) orcid page (here).

Working papers

Factors associated with human papillomavirus (HPV) virus load variations in genital infections in young women (pdf)
- Tessandier N.², Alizon S, medRXiv, 2025
Estimating HPV16 genome copy number per infected cell in cervical smears (pdf)
- Elie B, […] Tessandier N.²,³, Bravo IG³, medRXiv, 2024
Resource landscape shapes the composition and stability of the human vaginal microbiota (pdf)
- Kamyia T, Tessandier N., […] Alizon S, medRXiv, 2024
A novel RNP compartment boosts translation in growing mouse oocytes to avoid cytoplasm dilution (pdf)
- Zollo N., Zaffagnini G., Canette A., Letort G., Da Silva C., Tessandier N., […] Verlhac M.-H., Science (in review), 2025

Published articles

Atomic Force Microscopy reveals differences in mechanical properties linked to cortical structure in mouse and human oocytes (pdf)
- Bulteau R., Barbier L., Lamour G., Lemseffer G., Verlhac M.-H., Tessandier N., […], Campillo C., Small (accepted), 2025
High-risk human papillomavirus cervical infection prevalence in France, 2020-2023: a nationwide, large-scale, and spatially resolved study comparing opportunistic and organised screening (pdf)
- Supplisson O, Tessandier N., […], Alizon S, Eurosurveillance, (accepted) 2025
Viral and immune dynamics of genital human papillomavirus infections in young women with high temporal resolution (pdf)
- Tessandier N.¹², Elie B¹, […], Alizon S, PLOS Biol. , 2025
Factors shaping vaginal microbiota community long-term dynamics (pdf)
- Kamyia T, […] Tessandier N., […], Alizon S, PCI Microbiology, 2025
HPV detection patterns in young women from the PAPCLEAR longitudinal study: implications for HPV screening policies (pdf)
- Beneteau T, […] Tessandier N., Alizon S, Infect Dis (Lond), 2024
Does exposure to different menstrual products affect the vaginal environment? (pdf)
- Tessandier N.², […] Alizon S, Mol Ecol., 2023
Concomitant and productive genital infections by HSV-2 and HPV in two young women: A case report. (pdf)
- Uysal IB, […], Tessandier N.², IDCases, 2022
Platelets release mitochondrial antigens in systemic lupus erythematosus. (pdf)
- Melki I, Allaeys I, Tessandier N, […], Boilard E, Sci Transl Med, 2021
FcγRIIA expression accelerates nephritis and increases platelet activation in systemic lupus erythematosus. (pdf)
- Melki I, Allaeys I, Tessandier N, […], Boilard E, Sci Transl Med, 2020
Neuronal interleukin-1 receptors mediate pain in chronic inflammatory diseases. (pdf)
- Mailhot B, Christin M, Tessandier N, […], Lacroix S, J Exp Med. 2020
Platelets Disseminate Extracellular Vesicles in Lymph in Rheumatoid Arthritis. (pdf)
- Tessandier N, […], Boilard E, Arterioscler Thromb Vasc Biol. 2020
Extracellular vesicles are present in mouse lymph and their level differs in atherosclerosis. (pdf)
- Milasan A¹, Tessandier N¹, Martel C, J Extracell Vesicles. 2016

Reviews

Platelet microvesicles in health and disease. (pdf)
- Melki I¹, Tessandier N¹, Zufferey A, Boilard E, Platelets. 2017

¹: Co-first authorship

²: corresponding author

³: Co-last authorship

About me

nicolas.tessandier (/.at./) umontpellier.fr (Nicolas Tessandier) — Mon, 26 Aug 2024 15:18:28 +0200

I am a scientist, cyclist, advocate for a free internet, open-source enthusiast, mountain lover, humanist and conscious about the awaiting environmental collapse.

PhD

I did my PhD at Université Laval, in Québec, Canada. I am an immunologist by training. I studied platelets contribution to auto-immune diseases like rheumatoid arthritis or systemic lupus erythematosus. My PhD involved mostly experimental biology skills (e.g. flow cytometry).

In case you might want to read it, you can find my PhD here.

Post-Doc

I did my post doctoral training between Montpellier and Paris (Collège de France). There, I switched to research based on women’s health and methods based around data analysis.

I studied immune and viral mechanisms implicated in Human Papillomavirus (HPV) clearance.

During that time, I worked with unsupervised flow cytometry data analysis, longitudinal clinical data analysis, Bayesian statistics and bioinformatics.

Ongoing

Now, I work at the interface of experimental biology and quantitative biology, and I thrive to combine both to explore interesting scientific questions.

You can have a look at my CV.