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Isolation and functional characterization of primary human proximal tubular epithelial cells from brain-dead organ donors.

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The 5 matches
  1. [1] § MATERIALS AND METHODS › Knockdown of LRP2 by small interfering RNA ↔ bddPTC_analysis_PR_20260522.Rmd, lines 543–572 · score 0.74 · scramble RNA, qPCR, siRNA, mRNA, bddPTC, LRP2
  2. [2] § RESULTS › VCAM1 expression in bddPTC ↔ bddPTC_analysis_PR_20260522.Rmd, lines 869–915 · score 0.65 · human PT cell, VCAM1 expression, mRNA, S4, ANOVA
  3. [3] § MATERIALS AND METHODS › Reanalysis of mRNA expression in human cell lines ↔ bddPTC_analysis_PR_20260522.Rmd, lines 817–847 · score 0.63 · CLA, HKC11, HKC8, HPTC, LTR, ESBL
  4. [4] § RESULTS › Myoglobin uptake in bddPTC was decreased by inhibitors for megalin and endocytosis ↔ bddPTC_analysis_PR_20260522.Rmd, lines 543–572 · score 0.61 · scramble RNA, siRNA, mRNA, fold change, bddPTC, HK2
  5. [5] § MATERIALS AND METHODS › Statistical analysis ↔ bddPTC_analysis_PR_20260522.Rmd, lines 83–123 · score 0.50 · linear regression, bddPTC, interaction, Mb, HK2, uptake

Paper

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The authors' code

R Markdown · 942 lines · 47 KB · CC0-1.0 · 5 matches

  1. ---
  2. title: "bddPTC_analysis"
  3. output:
  4. html_document:
  5. toc: true
  6. toc_depth: 3
  7. theme: united
  8. date: "`r Sys.Date()`"
  9. editor_options:
  10. markdown:
  11. wrap: 72
  12. ---
  14. **bddPTC_analysis**
  16. ```{r read library, echo=F, warning=FALSE, message= FALSE, cache=TRUE}
  17. knitr::opts_chunk$set(echo = FALSE)
  18. ```
  20. Read data. Make figure for Mb uptake of bddPTC and HK2
  22. ```{r library, message= FALSE, warning = FALSE, fig.height=3.5, fig.width=4}
  23. library(dplyr)
  24. library(tidyverse)
  25. library(openxlsx) #install.packages("openxlsx")
  26. library(ggplot2)
  27. library(add2ggplot)#install.packages("add2ggplot")
  28. library(ggbeeswarm)
  29. library(car) #install.packages("car")
  30. ```
  32. ### [**1. bddPTC vs HK2 for Mg uptake**]{.underline}
  34. ```{r Mb long, message= FALSE, warning = FALSE, include=F, fig.height=3.5, fig.width=4}
  35. Mb1DF<-read.xlsx("Data_for_analysis.xlsx", sheet = "Mb1") %>% subset(ID!="comment")
  36. Mb1DF$group_pub <- ifelse(Mb1DF$group=="NHK", "bddPTC", "HK2")
  37. Mb1DF$group_pub <- factor(Mb1DF$group_pub, levels = c("bddPTC", "HK2"))
  39. Mb1DF$std_value2 <- ifelse(Mb1DF$group=="NHK",
  40. Mb1DF %>% subset(group=="NHK"& min==2) %>% .$value %>% mean(),
  41. Mb1DF %>% subset(group=="HK2"& min==2) %>% .$value %>% mean())
  42. Mb1DF$adjv2 <- Mb1DF$value/Mb1DF$std_value2
  43. Mb1DF_s <- Mb1DF %>% group_by(group_pub, min) %>% summarize(mean_adjv=mean(adjv), sd_adjv = sd(adjv), mean_adjv2=mean(adjv2), sd_adjv2 = sd(adjv2))
  45. Mb_plot <- function(DF, MAXMIN){
  46. ggplot(DF,aes(min, mean_adjv2, group=group_pub, color=group_pub, fill=group_pub))+
  47. geom_point(aes(shape=group_pub), size=3, position=position_dodge(width=MAXMIN/100))+
  48. geom_line(position=position_dodge(width=MAXMIN/100), size=1)+
  49. geom_errorbar(aes(ymin = mean_adjv2 - sd_adjv2, ymax = mean_adjv2 + sd_adjv2, width = MAXMIN/25), position=position_dodge(width=MAXMIN/100))+
  50. #geom_smooth(aes(color=group), method = lm, formula = y ~ poly(x, 2), size=1, se=T, alpha=0.15)+
  51. scale_shape_manual(values=c(16,17))+
  52. scale_color_manual(values = c("black", "red"))+
  53. coord_cartesian(xlim=c(0, MAXMIN), ylim=c(0.95, 1.2))+
  54. scale_x_continuous(breaks = c(0, 10, 30, 60, 90 ,120 ,150, 180), minor_breaks = seq(0, MAXMIN, by=2))+
  55. scale_y_continuous(breaks = seq(0.95, 1.2, by = 0.05))+
  56. labs(x="Time (min)", y="FITC Fluorescence (relative to baseline)")+
  57. theme_classic2()+
  58. theme(axis.line = element_line(color = "black", size=0.8), axis.text = element_text(color = "black", face="bold"),
  59. axis.title = element_text(color = "black", face="bold"),
  60. axis.ticks.length = unit(5, "pt"), axis.minor.ticks.length = rel(0.5),
  61. legend.position = c(0.15, 0.9), legend.title = element_blank(), legend.text = element_text(size=10, face="bold"))+
  62. guides(x=guide_axis(minor.ticks = T))
  63. }
  64. Mb_plot(Mb1DF_s, 120)+
  65. annotate("text", x=85, y=1.17, label="RM-ANOVA, P=0.76", fontface="bold")
  66. ```
  68. ```{r, Mb1 repeted ANOVA, warning=FALSE, include=F, message=F}
  69. for (i in c(2,10,30,60,120)) {
  70. shapiro_res <- shapiro.test(Mb1DF %>% subset(min==i) %>% .$adjv2) %>% .[["p.value"]]
  71. paste0(i, "min, Shapiro p=", round(shapiro_res, digits=3)) %>% print()
  72. }
  74. Mb1DF_RANOVA_DF <- Mb1DF %>% group_by(ID, group, min) %>% summarise(adjv2) %>% pivot_wider(names_from = min, values_from = adjv2, names_prefix = "m") %>% arrange(ID)
  76. time<- factor(c("m2", "m10", "m30", "m60", "m120"), levels = c("m2", "m10", "m30", "m60", "m120"))
  77. idata <- as.data.frame(time)
  78. model <- lm(cbind(m2, m10, m30, m60, m120) ~ group, data = Mb1DF_RANOVA_DF)
  79. res <- Anova(model, idata = idata, idesign = ~time, type = 3 )
  80. summary(res, multivariate = FALSE)
  81. ```
  83. #################################
  85. #### **Fig2E. bddPTC vs HK2 for Mg uptake (20 min)**
  87. #################################
  89. ```{r Mb 20min data, echo=T, message= F, warning = F, fig.height=3.5, fig.width=4}
  90. Mb2DF<-read.xlsx("Data_for_analysis.xlsx", sheet = "Mb2") %>% subset(ID!="comment" & min<20.1) #insignificant with 25min data
  91. Mb2DF$group_pub <- ifelse(Mb2DF$group=="NHK", "bddPTC", "HK2")
  92. Mb2DF$group_pub <- factor(Mb2DF$group_pub, levels = c("bddPTC", "HK2"))
  94. Mb2DF$std_value2 <- ifelse(Mb2DF$group=="NHK",
  95. Mb2DF %>% subset(group=="NHK"& min==2) %>% .$value %>% mean(),
  96. Mb2DF %>% subset(group=="HK2"& min==2) %>% .$value %>% mean())
  97. Mb2DF$adjv2 <- Mb2DF$value/Mb2DF$std_value2
  99. Mb2DF_s <- Mb2DF %>% group_by(group_pub, min) %>% summarize(mean_adjv=mean(adjv), sd_adjv = sd(adjv), mean_adjv2=mean(adjv2), sd_adjv2 = sd(adjv2))
  101. Mb_plot2 <- function(DF, MAXMIN){
  102. ggplot(DF,aes(min, adjv2, group=group_pub, color=group_pub))+
  103. geom_point(aes(shape=group_pub), size=2.5, position=position_dodge(width=MAXMIN/80))+
  104. #geom_line(position=position_dodge(width=MAXMIN/100))+
  105. #geom_errorbar(aes(ymin = mean_adjv - sd_adjv, ymax = mean_adjv + sd_adjv, width = MAXMIN/50), position=position_dodge(width=MAXMIN/100))+
  106. geom_smooth(aes(color=group_pub, fill=group_pub), method = lm, size=1, se=T, alpha=0.1)+ #formula = y ~ poly(x, 2),
  107. scale_shape_manual(values=c(16, 17))+
  108. scale_color_manual(values = c("black", "red"))+
  109. scale_fill_manual(values = c("black", "red"))+
  110. coord_cartesian(ylim=c(0.85, 1.25), xlim = c(0, MAXMIN))+
  111. scale_x_continuous(breaks = c(0, 5, 10, 15, 20), minor_breaks = seq(0, MAXMIN, by=1))+
  112. scale_y_continuous(breaks = seq(0.85, 1.25, by = 0.1))+
  113. labs(x="Time (min)", y="AFU relative to baseline")+
  114. theme_classic2()+
  115. theme(axis.line = element_line(color = "black", size=0.8), axis.text = element_text(color = "black", face="bold"),
  116. axis.title = element_text(color = "black", face="bold"),
  117. axis.ticks.length = unit(5, "pt"), axis.minor.ticks.length = rel(0.5),
  118. legend.position = c(0.15, 0.9), legend.title = element_blank(), legend.text = element_text(size=10, face="bold"))+
  119. guides(x=guide_axis(minor.ticks = T))
  120. }
  122. Mb_plot2(Mb2DF, 20)+ annotate("text", x=15, y=0.90, label="Linear regression, \nInteraction P=0.007")
  123. ```
  125. #################################
  127. #### **Fig2E. stats -\> bddPTC vs HK2 for Mg uptake (20 min)**
  129. #################################
  131. ```{r, Mb2 lm interaction, warning=FALSE, message=F}
  132. lm(formula=adjv2 ~ min, , data = Mb2DF %>% subset(group_pub=="bddPTC")) %>% broom::tidy()
  133. lm(formula=adjv2 ~ min, , data = Mb2DF %>% subset(group_pub=="HK2")) %>% broom::tidy()
  134. lm(formula=adjv2 ~ group_pub*min, data = Mb2DF) %>% broom::tidy()
  135. ```
  137. ```{r, Mb2 repeted ANOVA, warning=FALSE, message=F, include=F}
  138. #Shapiro test
  139. for (i in c(2,5,10,15,20)) {
  140. shapiro_res <- shapiro.test(Mb2DF %>% subset(group=="NHK"&min==i) %>% .$adjv2) %>% .[["p.value"]]
  141. paste0("NHK ", i, "min, Shapiro p=", round(shapiro_res, digits=3)) %>% print()
  142. shapiro_res <- shapiro.test(Mb2DF %>% subset(group=="HK2"&min==i) %>% .$adjv2) %>% .[["p.value"]]
  143. paste0("HK2 ", i, "min, Shapiro p=", round(shapiro_res, digits=3)) %>% print()
  144. }
  146. #ANCOVA
  147. Mb2DF_RANOVA_DF <- Mb2DF %>% group_by(ID, group, min) %>% summarise(adjv2) %>% pivot_wider(names_from = min, values_from = adjv2, names_prefix = "m") %>% arrange(ID)
  149. time <- factor(c("m2", "m5", "m10", "m15", "m20"), levels = c("m2", "m5", "m10", "m15", "m20"))
  150. idata <- as.data.frame(time)
  151. model <- lm(cbind(m2, m5, m10, m15, m20) ~ group, data = Mb2DF_RANOVA_DF)
  152. res <- Anova(model, idata = idata, idesign = ~time, type = 3 )
  153. summary(res, multivariate = FALSE)
  155. library(lme4)
  156. library(lmerTest)
  157. ggplot(Mb2DF, aes(min, adjv2, grup=group, color=group))+geom_smooth()+geom_point()+ylim(c(0, 1.2))
  158. lm(adjv2 ~ group*min, data = Mb2DF) %>% broom::tidy()
  159. lmer(adjv2 ~ group*min + (1|ID), data=Mb2DF, REML=TRUE) %>% summary() %>% .$coefficients
  160. ```
  162. ```{r lm, include=F}
  163. #lm comparison -\> bddPTC vs HK2 for Mg uptake (20 min)
  164. lm(adjv2 ~ min*group, data=Mb2DF) %>% summary() %>% .$coefficients %>% as.data.frame()
  165. ```
  167. ```{r lme, include=F}
  168. #dmh<-Mb2DF_RANOVA_DF%>%pivot_longer(3:7,names_to="time", values_to="afu", names_prefix="m")
  169. #mh<-aov(afu~group*time, data=dmh)
  170. #summary(mh)
  171. #lmer
  172. library(lme4) #install.packages("lme4")
  173. library(lmerTest) #install.packages("lmerTest")
  174. lmer(value ~ min*group + (1|ID), data=Mb2DF) %>% summary() %>% .$coefficients %>% as.data.frame()
  175. lmer(adjv ~ min*group + (1|ID), data=Mb2DF) %>% summary() %>% .$coefficients %>% as.data.frame()
  176. ```
  178. ### [**2. Dynasore andProchlorperazine**]{.underline}
  180. #################################
  182. #### **Fig3E. Mg uptake with dynasore**
  184. ```{r Dyna plot, message= FALSE, warning = FALSE, fig.height=3.5, fig.width=4}
  185. DynaDF <- read.xlsx("Data_for_analysis.xlsx", sheet = "Dyna") %>% subset(ID!="comment")
  186. DynaDF$group_pub <- ifelse(DynaDF$group=="NHK_Mb_Dy10", "Dynasore",
  187. ifelse(DynaDF$group=="NHK_Mb_Dy50", "Dynasore 50μM",
  188. ifelse(DynaDF$group=="NHK", "NHK",
  189. ifelse(DynaDF$group=="NHK_Mb", "NHK_Mb",
  190. ifelse(DynaDF$group=="onlyMb", "onlyMb",
  191. ifelse(DynaDF$group=="NHK_Mb_PCP", "Prochlorperazine",
  192. ifelse(DynaDF$group=="NHK_Mb_Veh", "Vehicle", "")))))))
  193. DynaDF$group_pub <- factor(DynaDF$group_pub, levels = c("onlyMb", "NHK", "NHK_Mb","Vehicle", "Dynasore", "Dynasore 50μM", "Prochlorperazine"))
  195. DynaDF$group_pub2 <- ifelse(DynaDF$group=="NHK_Mb_Dy10", "Dynasore 10μM",
  196. ifelse(DynaDF$group=="NHK_Mb_Dy50", "Dynasore 50μM",
  197. ifelse(DynaDF$group=="NHK_Mb_Veh", "Vehicle", NA)))
  198. DynaDF$group_pub2 <- factor(DynaDF$group_pub2, levels = c("Vehicle", "Dynasore 10μM", "Dynasore 50μM", NA))
  200. DynaDF$std_value2 <- ifelse(DynaDF$group=="NHK_Mb_Dy10", DynaDF %>% subset(group=="NHK_Mb_Dy10"& min==2) %>% .$value %>% mean(),
  201. ifelse(DynaDF$group=="NHK_Mb_Dy50", DynaDF %>% subset(group=="NHK_Mb_Dy50"& min==2) %>% .$value %>% mean(),
  202. ifelse(DynaDF$group=="NHK", DynaDF %>% subset(group=="NHK"& min==2) %>% .$value %>% mean(),
  203. ifelse(DynaDF$group=="NHK_Mb", DynaDF %>% subset(group=="NHK_Mb"& min==2) %>% .$value %>% mean(),
  204. ifelse(DynaDF$group=="onlyMb", DynaDF %>% subset(group=="onlyMb"& min==2) %>% .$value %>% mean(),
  205. ifelse(DynaDF$group=="NHK_Mb_PCP", DynaDF %>% subset(group=="NHK_Mb_PCP"& min==2) %>% .$value %>% mean(), ifelse(DynaDF$group=="NHK_Mb_Veh", DynaDF %>% subset(group=="NHK_Mb_Veh"& min==2) %>% .$value %>% mean(), NA)))))))
  207. DynaDF$adjv2 <- DynaDF$value/DynaDF$std_value2
  208. NHKwoMb_ave_DF <- DynaDF %>% subset(group=="NHK") %>% group_by(min) %>% summarise(woMb_ave=mean(adjv2))
  209. DynaDF2 <- left_join(DynaDF, NHKwoMb_ave_DF, by="min")
  210. DynaDF2$adjv3 <- DynaDF2$adjv2/DynaDF2$woMb_ave
  212. DynaDF2_s <- DynaDF2 %>% group_by(group_pub, group_pub2, min) %>% summarize(mean_adjv=mean(adjv), sd_adjv = sd(adjv),
  213. mean_adjv2=mean(adjv2), sd_adjv2 = sd(adjv2),
  214. mean_adjv3=mean(adjv3), sd_adjv3 = sd(adjv3))
  217. shapeset1 <- c(15,16,17,17,14,16,20)
  218. colorset1 <- c("gray30","orange","darkred","red","chartreuse4","darkgreen","deepskyblue")
  220. Dyna_plot <- function(DF, GROUP_PUB, MEAN, SD, MAXMIN, SHAPESET, COLORSET){
  221. ggplot(DF,aes(min, {{MEAN}}, group={{GROUP_PUB}}, color={{GROUP_PUB}}, fill={{GROUP_PUB}}))+
  222. geom_point(aes(shape={{GROUP_PUB}}), size=3, position=position_dodge(width=MAXMIN/100))+
  223. geom_smooth(method = lm, formula = y ~ poly(x, 2), size=1, se=T, alpha=0.2)+ #locally weighted scatter plot smooth
  224. geom_errorbar(aes(ymin = {{MEAN}} - {{SD}}, ymax = {{MEAN}} + {{SD}}, width = MAXMIN/50), position=position_dodge(width=MAXMIN/100))+
  225. scale_shape_manual(values=SHAPESET)+
  226. scale_color_manual(values=COLORSET)+
  227. scale_fill_manual(values=COLORSET)+
  228. coord_cartesian(xlim=c(0, MAXMIN), ylim=c(0.95, 1.2))+
  229. theme_classic2()+
  230. scale_x_continuous(breaks = c(0, 30, 60, 90 ,120 ,150, 180), minor_breaks = seq(0, MAXMIN, by=2))+
  231. scale_y_continuous(breaks = seq(0.85, 1.2, by = 0.05))+
  232. labs(x="Time (min)", y="AFU relative to baseline")+
  233. theme_classic2()+
  234. theme(axis.line = element_line(color = "black", size=0.8), axis.text = element_text(color = "black", face="bold"),
  235. axis.title = element_text(color = "black", face="bold"),
  236. axis.ticks.length = unit(5, "pt"), axis.minor.ticks.length = rel(0.5),
  237. legend.position = c(0.25, 0.83), legend.title = element_blank(), legend.text = element_text(size=10, face="bold"))+
  238. guides(x=guide_axis(minor.ticks = T))
  239. }
  242. shapeset2 <- c(16, 15)
  243. colorset2 <- c("black", "darkgreen")
  244. Dyna_plot(DynaDF2_s %>% subset(group_pub=="Vehicle"|group_pub=="Dynasore"), group_pub, mean_adjv3, sd_adjv3, 180, shapeset2, colorset2)+
  245. annotate("text", x=140, y=1.02, label="RM-ANOVA, \nInteraction P<0.001")
  246. ```
  248. ```{r, other dyna plot, include=F}
  249. Dyna_plot(DynaDF2_s, group_pub, mean_adjv2, sd_adjv2, 180, shapeset1, colorset1)
  250. Dyna_plot(DynaDF2_s, group_pub, mean_adjv3, sd_adjv3, 180, shapeset1, colorset1)
  251. shapeset2B <- c(16, 15, 15)
  252. colorset2B <- c("black", "darkgreen", "chartreuse4")
  253. Dyna_plot(DynaDF2_s %>% subset(!is.na(group_pub2)), group_pub2, mean_adjv3, sd_adjv3, 180, shapeset2B, colorset2B)+
  254. annotate("text", x=120, y=0.98, label="RM-ANOVA, P<0.001")
  255. ```
  257. #################################
  259. #### **Fig3E. stats**
  261. #################################
  263. ```{r, Dyna repeted ANOVA, warning=FALSE, message=F}
  264. DynaDF3 <- DynaDF2 %>% subset(group=="NHK_Mb"|group=="NHK_Mb_Dy10") %>% mutate("min2"=min^0.5)
  265. #Shapiro test
  266. for (i in c(2,10,20,30,60,90,120,150,180)) {
  267. shapiro_res <- shapiro.test(DynaDF3 %>% subset(group=="NHK_Mb"&min==i) %>% .$adjv2) %>% .[["p.value"]]
  268. paste0("NHK ", i, "min, Shapiro p=", round(shapiro_res, digits=4)) %>% print()
  269. shapiro_res <- shapiro.test(DynaDF3 %>% subset(group=="NHK_Mb_Dy10"&min==i) %>% .$adjv2) %>% .[["p.value"]]
  270. paste0("D10 ", i, "min, Shapiro p=", round(shapiro_res, digits=4)) %>% print()
  271. }
  273. DynaDF3_RANOVA_DF <- DynaDF3 %>% group_by(ID, group, min) %>% summarise(adjv3) %>% pivot_wider(names_from = min, values_from = adjv3, names_prefix = "m") %>% arrange(ID)
  275. #RM-ANOVA
  276. time<- factor(c("m2", "m10", "m20", "m30", "m60", "m90", "m120", "m150", "m180"), levels = c("m2", "m10", "m20", "m30", "m60", "m90", "m120", "m150", "m180"))
  277. idata <- as.data.frame(time)
  278. model <- lm(cbind(m2, m10, m20, m30, m60, m90, m120, m150, m180) ~ group, data = DynaDF3_RANOVA_DF)
  279. res <- Anova(model, idata = idata, idesign = ~time, type = 3 )
  280. summary(res, multivariate = FALSE)
  281. ```
  282. ```{r, Dyna compare with time^1/2, include=F}
  283. ggplot(DynaDF3, aes(min^0.5, adjv3, grup=group_pub, color=group_pub))+geom_smooth(method = "lm")+geom_point()
  284. lm(adjv3 ~ group_pub*min2, data = DynaDF3) %>% broom::tidy()
  285. lmer(adjv3 ~ group_pub*min2 + (1|ID), data=DynaDF3, REML=TRUE) %>% summary() %>% .$coefficients
  286. ```
  288. ```{r, Dyna repeted ANOVA2, warning=FALSE, message=F, include=F}
  289. library(emmeans) #install.packages("emmeans")
  290. DynaDF3_long <- DynaDF3 %>% dplyr::select(ID, group, min, adjv3) %>% mutate(min = factor(min, levels = c(2,10,20,30,60,90,120,150,180)))
  291. aov_model <- aov(adjv3 ~ group*min + Error(ID/min), data = DynaDF3_long)
  292. emm <- emmeans(aov_model, ~ group)
  293. pairs(emm, adjust = "tukey")
  294. ```
  296. #################################
  298. #### **Fig3F. Mg uptake with** **Prochlorperazine**
  300. #################################
  302. ```{r PCP plot, message= FALSE, warning = FALSE, fig.height=3.5, fig.width=4}
  303. shapeset3 <- c(16,15)
  304. colorset3 <- c("black", "blue")
  305. Dyna_plot(DynaDF2_s %>% subset(group_pub=="Vehicle"|group_pub=="Prochlorperazine"), group_pub, mean_adjv3, sd_adjv3, 180, shapeset3, colorset3) +
  306. annotate("text", x=130, y=1.03, label="RM-ANOVA, \nInteraction P<0.001")
  307. ```
  309. #################################
  311. #### **Fig3F. stats**
  313. #################################
  315. ```{r, PCP repeted ANOVA, warning=FALSE, message=F}
  316. PCPDF <- DynaDF2 %>% subset(group=="NHK_Mb"|group=="NHK_Mb_PCP") %>% mutate("min2"=min^0.5)
  317. #Shapiro test
  318. for (i in c(2,10,20,30,60,90,120,150,180)) {
  319. shapiro_res <- shapiro.test(PCPDF %>% subset(group=="NHK_Mb"&min==i) %>% .$adjv3) %>% .[["p.value"]]
  320. paste0("NHK ", i, "min, Shapiro p=", round(shapiro_res, digits=4)) %>% print()
  321. shapiro_res <- shapiro.test(PCPDF %>% subset(group=="NHK_Mb_PCP"&min==i) %>% .$adjv3) %>% .[["p.value"]]
  322. paste0("PCP ", i, "min, Shapiro p=", round(shapiro_res, digits=4)) %>% print()
  323. }
  325. #RM-ANOVA
  326. PCPDF_RANOVA_DF <- PCPDF %>% group_by(ID, group, min) %>% summarise(adjv2) %>% pivot_wider(names_from = min, values_from = adjv2, names_prefix = "m") %>% arrange(ID)
  328. time<- factor(c("m2", "m10", "m20", "m30", "m60", "m90", "m120", "m150", "m180"), levels = c("m2", "m10", "m20", "m30", "m60", "m90", "m120", "m150", "m180"))
  329. idata <- as.data.frame(time)
  330. model <- lm(cbind(m2, m10, m20, m30, m60, m90, m120, m150, m180) ~ group, data = PCPDF_RANOVA_DF)
  331. res <- Anova(model, idata = idata, idesign = ~time, type = 3 )
  332. summary(res, multivariate = FALSE)
  333. ```
  334. ```{r, PCP compare with time^1/2, include=F}
  335. ggplot(PCPDF, aes(min2, adjv3, grup=group_pub, color=group_pub))+geom_smooth(method = "lm")+geom_point()
  336. lm(adjv3 ~ group_pub*min2, data = PCPDF) %>% broom::tidy()
  337. lmer(adjv3 ~ group_pub*min2 + (1|ID), data=PCPDF, REML=TRUE) %>% summary() %>% .$coefficients
  338. ```
  340. ```{r Dyna plot2, message= FALSE, warning = FALSE, include=F, fig.height=3.5, fig.width=4}
  341. #Mg uptake with Dyansore/PCP (short time)
  342. Dyna_plot2 <- function(DF, MEAN, SD, MAXMIN, SHAPESET, COLORSET){
  343. ggplot(DF,aes(min, {{MEAN}}, group=group_pub, color=group_pub, fill=group_pub))+
  344. geom_point(aes(shape=group_pub), size=3, position=position_dodge(width=MAXMIN/100))+
  345. #geom_line(position=position_dodge(width=MAXMIN/100))+
  346. geom_smooth(method = lm, formula = y ~ poly(x, 2), size=1, se=T, alpha=0.2)+ #locally weighted scatter plot smooth
  347. geom_errorbar(aes(ymin = {{MEAN}} - {{SD}}, ymax = {{MEAN}} + {{SD}}, width = MAXMIN/50), position=position_dodge(width=MAXMIN/100))+
  348. scale_shape_manual(values=SHAPESET)+
  349. scale_color_manual(values=COLORSET)+
  350. scale_fill_manual(values=COLORSET)+
  351. coord_cartesian(xlim=c(0, MAXMIN), ylim=c(0.95, 1.05))+
  352. theme_classic2()+
  353. scale_x_continuous(breaks = c(0, 5 ,10, 15, 20), minor_breaks = seq(0, MAXMIN, by=2))+
  354. scale_y_continuous(breaks = seq(0.95, 1.2, by = 0.05))+
  355. labs(x="Time (min)", y="FITC Fluorescence (relative to baseline)")+
  356. theme_classic2()+
  357. theme(axis.line = element_line(color = "black", size=0.8), axis.text = element_text(color = "black", face="bold"),
  358. axis.title = element_text(color = "black", face="bold"),
  359. axis.ticks.length = unit(5, "pt"), axis.minor.ticks.length = rel(0.5),
  360. legend.position = c(0.25, 0.8), legend.title = element_blank(), legend.text = element_text(size=10, face="bold"))+
  361. guides(x=guide_axis(minor.ticks = T))
  362. }
  364. Dyna_plot2(DynaDF2_s %>% subset(group_pub=="Vehicle"|group_pub=="Dynasore 10μM"|group_pub=="Dynasore 50μM") %>% subset(min<21), mean_adjv3, sd_adjv3, 20, shapeset2, colorset2)
  365. Dyna_plot2(DynaDF2_s %>% subset(group_pub=="Vehicle"|group_pub=="Prochlorperazine") %>% subset(min<21), mean_adjv3, sd_adjv3, 20, shapeset3, colorset3)
  366. ```
  368. ### [**3. LRP2 and AQP1 western blot**]{.underline}
  370. #### **Fig2B. LRP2 western blot**
  372. #################################
  374. ```{r LRP2WB, warning=F, message=F, fig.height=3.5, fig.width=4}
  375. LRP2WBDF<-read.xlsx("Data_for_analysis.xlsx", sheet = "LRP2WB") %>% subset(ID!="comment" & group!="PC1" & group!="PC2")
  376. LRP2WBDF$group_pub <- ifelse(LRP2WBDF$group=="hNSKC_p0", "bddKC",
  377. ifelse(LRP2WBDF$group=="hPTEC", "bddPTC",
  378. ifelse(LRP2WBDF$group=="HK2", "HK2", "others")))
  379. LRP2WBDF$group_pub <- factor(LRP2WBDF$group_pub, levels=c("bddKC", "bddPTC", "HK2"))
  381. ggplot(LRP2WBDF, aes(group_pub, adjv))+
  382. geom_boxplot()+
  383. geom_quasirandom(aes(shape=group_pub), position=position_jitter(height = 0), size=3, width=0.15)+
  384. #scale_color_manual(values = c("darkred","red4", "red", "blue"))+
  385. scale_shape_manual(values=c(18, 16, 17))+
  386. coord_cartesian(ylim=c(0, 2))+
  387. #scale_y_continuous(breaks = seq(0.90, 1.15, by = 0.05))+
  388. labs(x="", y="Protein abundance (fold change)")+
  389. theme_classic2()+
  390. theme(axis.line = element_line(color = "black", size=0.8),
  391. axis.text = element_text(color = "black", face="bold", size=12),
  392. axis.title = element_text(color = "black", face="bold"),
  393. legend.position = "none", legend.title = element_blank(), legend.text = element_text(size=12, face="bold"))+
  394. annotate("text", x=3, y=1.3, label="ANOVA\nP=0.02", size=3.5)#, fontface="bold")
  395. ```
  397. #################################
  399. #### **Fig2B. stats**
  401. #################################
  403. ```{r, LRP2 ANOVA, message=FALSE}
  404. #mean(SD)
  405. LRP2WBDF %>% group_by(group) %>% summarize(mean=mean(adjv), sd=sd(adjv))
  407. #ANOVA aand Tukey
  408. model<- aov(adjv ~ group, data = LRP2WBDF)
  409. model %>% summary()
  410. TukeyHSD(model)
  411. ```
  412. #################################
  414. #### **Supplementary fig S3. LRP2-passage western blot**
  416. #################################
  418. ```{r LRP2passWB, warning=F, message=F, fig.height=4, fig.width=4.5}
  419. LRP2passWBDF<-read.xlsx("Data_for_analysis.xlsx", sheet = "LRP2_passWB") %>% subset(ID!="comment")
  420. LRP2passWBDF$group_pub <- LRP2passWBDF$group
  422. ggplot(LRP2passWBDF, aes(group_pub, adjv))+
  423. geom_boxplot()+
  424. geom_quasirandom(aes(shape=group_pub), position=position_jitter(height = 0), size=3, width=0.15)+
  425. #scale_color_manual(values = c("darkred","red4", "red", "blue"))+
  426. scale_shape_manual(values=c(16, 16, 16))+
  427. coord_cartesian(ylim=c(0, 2))+
  428. #scale_y_continuous(breaks = seq(0.90, 1.15, by = 0.05))+
  429. labs(x="", y="Protein abundance (fold change)")+
  430. theme_classic2()+
  431. theme(axis.line = element_line(color = "black", size=0.8),
  432. axis.text = element_text(color = "black", face="bold", size=12),
  433. axis.title = element_text(color = "black", face="bold"),
  434. legend.position = "none", legend.title = element_blank(), legend.text = element_text(size=12, face="bold"))+
  435. annotate("text", x=2.5, y=1.8, label="ANOVA\nP=0.37", size=3.5)#, fontface="bold")
  436. ```
  438. #################################
  440. #### **Supplementary figure S3. stats**
  442. #################################
  444. ```{r, LRP2pass ANOVA, message=FALSE}
  445. #mean(SD)
  446. LRP2passWBDF %>% group_by(group) %>% summarize(mean=mean(adjv), sd=sd(adjv))
  448. #ANOVA aand Tukey
  449. model<- aov(adjv ~ group, data = LRP2passWBDF)
  450. model %>% summary()
  451. TukeyHSD(model)
  452. ```
  454. #################################
  456. #### **Fig2C. AQP1 western blot**
  458. #################################
  460. ```{r AQP1WB, warning=F, message=F, fig.height=3.5, fig.width=4}
  461. AQP1WBDF<-read.xlsx("Data_for_analysis.xlsx", sheet = "AQP1WB") %>% subset(ID!="comment" & group!="PC1" & group!="PC2")
  462. AQP1WBDF$group_pub <- ifelse(AQP1WBDF$group=="hNSKC_p0", "bddKC",
  463. ifelse(AQP1WBDF$group=="hPTEC", "bddPTC",
  464. ifelse(AQP1WBDF$group=="HK2", "HK2", "others")))
  465. AQP1WBDF$group_pub <- factor(AQP1WBDF$group_pub, levels=c("bddKC", "bddPTC", "HK2"))
  467. ggplot(AQP1WBDF, aes(group_pub, adjv))+
  468. geom_boxplot()+
  469. geom_quasirandom(aes(shape=group_pub), position=position_jitter(height = 0), size=3, width=0.15)+
  470. #scale_color_manual(values = c("darkred","red4", "red", "blue"))+
  471. scale_shape_manual(values=c(18, 16, 17))+
  472. coord_cartesian(ylim=c(0, 2))+
  473. #scale_y_continuous(breaks = seq(0.90, 1.15, by = 0.05))+
  474. labs(x="", y="Protein abundance (fold change)")+
  475. theme_classic2()+
  476. theme(axis.line = element_line(color = "black", size=0.8),
  477. axis.text = element_text(color = "black", face="bold", size=12),
  478. axis.title = element_text(color = "black", face="bold"),
  479. legend.position = "none", legend.title = element_blank(), legend.text = element_text(size=12, face="bold"))+
  480. annotate("text", x=2.5, y=1.6, label="ANOVA\nP=0.08", size=3.5)#, fontface="bold")
  481. ```
  483. #################################
  485. #### **Fig2C. stats**
  487. #################################
  489. ```{r, AQP1 ANOVA, message=FALSE}
  490. #mean(SD)
  491. AQP1WBDF %>% group_by(group) %>% summarize(mean=mean(adjv), sd=sd(adjv))
  493. #ANOVA aand Tukey
  494. model<- aov(adjv ~ group, data = AQP1WBDF)
  495. model %>% summary()
  496. TukeyHSD(model)
  497. ```
  498. #################################
  500. #### **Fig2D. ZO1 western blot**
  502. #################################
  504. ```{r ZO1WB, warning=F, message=F, fig.height=3.5, fig.width=4}
  505. ZO1WBDF<-read.xlsx("Data_for_analysis.xlsx", sheet = "ZO1WB") %>% subset(ID!="comment" & group!="PC1" & group!="PC2")
  506. ZO1WBDF$group_pub <- ifelse(ZO1WBDF$group=="hNSKC_p0", "bddKC",
  507. ifelse(ZO1WBDF$group=="hPTEC", "bddPTC",
  508. ifelse(ZO1WBDF$group=="HK2", "HK2", "others")))
  509. ZO1WBDF$group_pub <- factor(ZO1WBDF$group_pub, levels=c("bddKC", "bddPTC", "HK2"))
  511. ggplot(ZO1WBDF, aes(group_pub, adjv))+
  512. geom_boxplot()+
  513. geom_quasirandom(aes(shape=group_pub), position=position_jitter(height = 0), size=3, width=0.15)+
  514. #scale_color_manual(values = c("darkred","red4", "red", "blue"))+
  515. scale_shape_manual(values=c(18, 16, 17))+
  516. coord_cartesian(ylim=c(0, 2))+
  517. #scale_y_continuous(breaks = seq(0.90, 1.15, by = 0.05))+
  518. labs(x="", y="Protein abundance (fold change)")+
  519. theme_classic2()+
  520. theme(axis.line = element_line(color = "black", size=0.8),
  521. axis.text = element_text(color = "black", face="bold", size=12),
  522. axis.title = element_text(color = "black", face="bold"),
  523. legend.position = "none", legend.title = element_blank(), legend.text = element_text(size=12, face="bold"))+
  524. annotate("text", x=2.5, y=1.75, label="ANOVA\nP=0.46", size=3.5)#, fontface="bold")
  525. ```
  527. #################################
  529. #### **Fig2D. stats**
  531. #################################
  533. ```{r, ZO1 ANOVA, message=FALSE}
  534. #mean(SD)
  535. ZO1WBDF %>% group_by(group) %>% summarize(mean=mean(adjv), sd=sd(adjv))
  537. #ANOVA aand Tukey
  538. model<- aov(adjv ~ group, data = ZO1WBDF)
  539. model %>% summary()
  540. TukeyHSD(model)
  541. ```
  543. #################################
  545. ### [**4. LRP2 qPCR with siRNA**]{.underline}
  547. #### **Fig3c. LRP2 mRNA with siRNA**
  549. #################################
  551. ```{r qPCR siRNA, message=F, warning=F, fig.height=3.5, fig.width=4}
  552. qpcrDF<-read.xlsx("Data_for_analysis.xlsx", sheet = "siRNA_qPCR") %>% subset(ID!="comment")
  553. qpcrDF$group_pub <- ifelse(qpcrDF$group=="NHK sc", "bddPTC\n+scramble RNA",
  554. ifelse(qpcrDF$group=="NHK KO", "bddPTC\n+siRNA",
  555. ifelse(qpcrDF$group=="HK2", "HK2\n+scramble RNA", "ohter")))
  556. qpcrDF$group_pub <- factor(qpcrDF$group_pub, levels = c("bddPTC\n+scramble RNA", "bddPTC\n+siRNA", "HK2\n+scramble RNA"))
  558. ggplot(qpcrDF, aes(group_pub, adjv))+
  559. geom_boxplot()+
  560. geom_quasirandom(aes(shape=group_pub), position=position_jitter(height = 0), size=3, width=0.15)+
  561. scale_shape_manual(values=c(16, 15, 17))+
  562. coord_cartesian(ylim=c(0, 1.5))+
  563. labs(x="", y="mRNA expression (fold change)")+
  564. theme_classic2()+
  565. theme(axis.line = element_line(color = "black", size=0.8),
  566. axis.text = element_text(color = "black", face="bold", size=10),
  567. axis.title = element_text(color = "black", face="bold", size=12),
  568. legend.position = "none",
  569. plot.title = element_text(hjust=0.5, size=14, face="bold.italic"))+
  570. ggtitle("LRP2")+
  571. annotate("text", x=3, y=0.8, label="ANOVA\nP<0.001", size=3.5)#, fontface="bold")
  572. ```
  574. #################################
  576. #### **Fig3C. stats**
  578. #################################
  580. ```{r, qPCR siRNA ANOVA, meassage=F}
  581. #ANOVA and Tukey
  582. model<- aov(adjv ~ group, data = qpcrDF)
  583. model %>% summary()
  584. TukeyHSD(model)
  585. ```
  587. #################################
  589. #### **Fig3D. Mg uptake with Lrp2 siRNA (titration)**
  591. #################################
  593. ```{r siRNA, message=F, warning=F, fig.height=4, fig.width=7}
  594. siDF<-read.xlsx("Data_for_analysis.xlsx", sheet = "siRNA") %>% subset(ID!="comment")
  595. siDF$std_value2 <- ifelse(siDF$group=="NHK_DMEM_stv", siDF %>% subset(group=="NHK_DMEM_stv"& min==2) %>% .$value %>% mean(),
  596. ifelse(siDF$group=="NHK_OMEM_stv", siDF %>% subset(group=="NHK_OMEM_stv"& min==2) %>% .$value %>% mean(),
  597. ifelse(siDF$group=="NHK_Mb_DMEM_stv", siDF %>% subset(group=="NHK_Mb_DMEM_stv"& min==2) %>% .$value %>% mean(),
  598. ifelse(siDF$group=="NHK_Mb_OMEM_stv", siDF %>% subset(group=="NHK_Mb_OMEM_stv"& min==2) %>% .$value %>% mean(),
  599. ifelse(siDF$group=="NHK_Mb_scramble2uL", siDF %>% subset(group=="NHK_Mb_scramble2uL"& min==2) %>% .$value %>% mean(), ifelse(siDF$group=="NHK_Mb_scramble4uL", siDF %>% subset(group=="NHK_Mb_scramble4uL"& min==2) %>% .$value %>% mean(),
  600. ifelse(siDF$group=="NHK_Mb_scramble8uL", siDF %>% subset(group=="NHK_Mb_scramble8uL"& min==2) %>% .$value %>% mean(),
  601. ifelse(siDF$group=="NHK_Mb_scramble16uL", siDF %>% subset(group=="NHK_Mb_scramble16uL"& min==2) %>% .$value %>% mean(),
  602. ifelse(siDF$group=="NHK_Mb_siRNA2uL", siDF %>% subset(group=="NHK_Mb_siRNA2uL"& min==2) %>% .$value %>% mean(),
  603. ifelse(siDF$group=="NHK_Mb_siRNA4uL", siDF %>% subset(group=="NHK_Mb_siRNA4uL"& min==2) %>% .$value %>% mean(), ifelse(siDF$group=="NHK_Mb_siRNA8uL", siDF %>% subset(group=="NHK_Mb_siRNA8uL"& min==2) %>% .$value %>% mean(),
  604. ifelse(siDF$group=="NHK_Mb_siRNA16uL", siDF %>% subset(group=="NHK_Mb_siRNA16uL"& min==2) %>% .$value %>% mean(),
  605. ifelse(siDF$group=="Mb", siDF %>% subset(group=="Mb"& min==2) %>% .$value %>% mean(), NA)))))))))))))
  607. siDF$adjv2 <- siDF$value/siDF$std_value2
  608. siDF$group_pub <- ifelse(siDF$group=="NHK_Mb_scramble2uL", "Scramble RNA",
  609. ifelse(siDF$group=="NHK_Mb_scramble4uL", "Scramble RNA",
  610. ifelse(siDF$group=="NHK_Mb_scramble8uL", "Scramble RNA",
  611. ifelse(siDF$group=="NHK_Mb_scramble16uL", "Scramble RNA",
  612. ifelse(siDF$group=="NHK_Mb_siRNA2uL", "LRP2 siRNA",
  613. ifelse(siDF$group=="NHK_Mb_siRNA4uL", "LRP2 siRNA",
  614. ifelse(siDF$group=="NHK_Mb_siRNA8uL", "LRP2 siRNA",
  615. ifelse(siDF$group=="NHK_Mb_siRNA16uL", "LRP2 siRNA",
  616. ifelse(siDF$group=="Mb", "onlyMb", "others")))))))))
  618. siDF$group_pub <- factor(siDF$group_pub, levels = c("Scramble RNA", "LRP2 siRNA", "others"))
  620. siDF_s <- siDF %>% group_by(group, min, group_pub) %>% summarize(mean_adjv=mean(adjv), sd_adjv = sd(adjv), mean_adjv2=mean(adjv2), sd_adjv2 = sd(adjv2))
  622. si_plot <- function(DF, MAXMIN){
  623. ggplot(DF,aes(min, mean_adjv, group=group, color=group))+
  624. geom_point(size=2, position=position_dodge(width=MAXMIN/100))+
  625. geom_errorbar(aes(ymin = mean_adjv - sd_adjv, ymax = mean_adjv + sd_adjv, width = MAXMIN/50), position=position_dodge(width=MAXMIN/100))+
  626. geom_smooth(method = lm, formula = y ~ poly(x, 2), se=F)+
  627. theme_classic2()+
  628. coord_cartesian(xlim=(c(0, MAXMIN)))
  629. }
  630. ```
  632. #################################
  634. ```{r siRNA2, message=F, warning=F, fig.height=3.5, fig.width=4}
  635. si_plot2 <- function(DF, MAXMIN){
  636. ggplot(DF,aes(min, mean_adjv2, group=group_pub, color=group_pub, fill=group_pub))+
  637. geom_point(aes(shape=group_pub), size=3, position=position_dodge(width=MAXMIN/100))+
  638. #geom_line(position=position_dodge(width=MAXMIN/100))+
  639. geom_errorbar(aes(ymin = mean_adjv2 - sd_adjv2, ymax = mean_adjv2 + sd_adjv2, width = MAXMIN/50), position=position_dodge(width=MAXMIN/100))+
  640. geom_smooth(method=lm, formula = y ~ poly(x, 2), size=1, se=T, alpha=0.15)+
  641. scale_shape_manual(values=c(16,15))+
  642. scale_color_manual(values = c("black","purple4"))+
  643. scale_fill_manual(values = c("black","purple4"))+
  644. theme_classic2()+
  645. coord_cartesian(xlim=c(0, MAXMIN), ylim=c(0.95, 1.2))+
  646. scale_x_continuous(breaks = c(0, 30, 60, 90 ,120 ,150, 180), minor_breaks = seq(0, MAXMIN, by=5))+
  647. scale_y_continuous(breaks = seq(0.95, 1.2, by = 0.05))+
  648. labs(x="Time (min)", y="AFU relative to baseline")+
  649. theme_classic2()+
  650. theme(axis.line = element_line(color = "black", size=0.8), axis.text = element_text(color = "black", face="bold"),
  651. axis.title = element_text(color = "black", face="bold"),
  652. axis.ticks.length = unit(5, "pt"), axis.minor.ticks.length = rel(0.5),
  653. legend.position = c(0.25, 0.8), legend.title = element_blank(), legend.text = element_text(size=10, face="bold"))+
  654. guides(x=guide_axis(minor.ticks = T))
  655. }
  656. #si_plot2(siDF_s %>% subset(group=="NHK_Mb_scramble4uL"|group=="NHK_Mb_siRNA4uL"), 180)+ annotate("text", x=135, y=1.03, label="RM-ANOVA, P=0.08")+ ggtitle("Scramble RNA / siRNA, 4 uL")+ theme(plot.title = element_text(hjust=0.5, vjust=-1, face="bold"))
  658. #si_plot2(siDF_s %>% subset(group=="NHK_Mb_scramble8uL"|group=="NHK_Mb_siRNA8uL"), 180)+ annotate("text", x=135, y=1.03, label="RM-ANOVA, P=0.008")+ ggtitle("Scramble RNA / siRNA, 8 uL")+ theme(plot.title = element_text(hjust=0.5, vjust=-1, face="bold"))
  660. si_plot2(siDF_s %>% subset(group=="NHK_Mb_scramble16uL"|group=="NHK_Mb_siRNA16uL"), 180)+
  661. annotate("text", x=130, y=1.02, label="RM-ANOVA, \nInteraction P<0.001")
  662. ```
  664. #################################
  666. #### **Figure3D. stats**
  668. #################################
  670. ```{r, siRNA repeated ANOVA, message=FALSE}
  671. #Shapiro test
  672. for (i in c(2, 10,20,30,60,90,120,150,180)) {
  673. shapiro_res <- shapiro.test(siDF %>% subset(group=="NHK_Mb_scramble16uL"|group=="NHK_Mb_siRNA16uL") %>% subset(min==i) %>% .$adjv2) %>% .[["p.value"]]
  674. paste0(i, "min, Shapiro p=", round(shapiro_res, digits=3)) %>% print()
  675. }
  677. #RM-ANOVA
  678. siDF_RANOVA_DF <- siDF %>% subset(group=="NHK_Mb_scramble16uL"|group=="NHK_Mb_siRNA16uL") %>%
  679. group_by(ID, group, min) %>% summarise(adjv2) %>%
  680. pivot_wider(names_from = min, values_from = adjv2, names_prefix = "m") %>% arrange(ID)
  682. time<- factor(c("m2", "m10", "m20", "m30", "m60", "m90","m120","m150", "m180"), levels = c("m2", "m10", "m20", "m30", "m60", "m90","m120","m150", "m180"))
  683. idata <- as.data.frame(time)
  684. model <- lm(cbind(m2, m10, m20, m30, m60, m90, m120, m150, m180) ~ group, data = siDF_RANOVA_DF)
  685. res <- Anova(model, idata = idata, idesign = ~time, type = 3 )
  686. summary(res, multivariate = FALSE)
  687. ```
  688. ```{r, siRNA compare with time^1/2, include=F}
  689. siDF2 <- siDF %>% subset(group=="NHK_Mb_scramble16uL"|group=="NHK_Mb_siRNA16uL") %>% mutate("min2"=min^0.5)
  690. ggplot(siDF2, aes(min2, adjv2, grup=group_pub, color=group_pub))+geom_smooth(method = "lm")+geom_point()
  691. lm(adjv2 ~ group_pub*min2, data = siDF2) %>% broom::tidy()
  692. lmer(adjv2 ~ group_pub*min2 + (1|ID), data=siDF2, REML=TRUE) %>% summary() %>% .$coefficients
  693. ```
  695. ```{r siRNA3, message=F, warning=F, include=F, fig.height=3.5, fig.width=4}
  696. #Mg uptake with Megaline-siRNA16uL (short time)
  697. si_plot3 <- function(DF, MAXMIN){
  698. ggplot(DF,aes(min, mean_adjv2, group=group_pub, fill=group_pub))+
  699. geom_point(aes(shape=group_pub), size=3, position=position_dodge(width=MAXMIN/100))+
  700. geom_errorbar(aes(ymin = mean_adjv2 - sd_adjv2, ymax = mean_adjv2 + sd_adjv2, width = MAXMIN/50), position=position_dodge(width=MAXMIN/100))+
  701. geom_smooth(method=lm, formula = y ~ poly(x, 2), size=1, se=T, alpha=0.15)+
  702. theme_classic2()+
  703. coord_cartesian(xlim=c(0, MAXMIN), ylim=c(0.95, 1.1))+
  704. scale_x_continuous(breaks = c(0, 10, 20), minor_breaks = seq(0, MAXMIN, by=2))+
  705. scale_y_continuous(breaks = seq(0.95, 1.1, by = 0.05))+
  706. labs(x="Time (min)", y="FITC Fluorescence (relative to baseline)")+
  707. theme_classic2()+
  708. theme(axis.line = element_line(color = "black", size=0.8), axis.text = element_text(color = "black", face="bold"),
  709. axis.title = element_text(color = "black", face="bold"),
  710. axis.ticks.length = unit(5, "pt"), axis.minor.ticks.length = rel(0.5),
  711. legend.position = c(0.25, 0.8), legend.title = element_blank(), legend.text = element_text(size=10, face="bold"))+
  712. guides(x=guide_axis(minor.ticks = T))
  713. }
  714. si_plot3(siDF_s %>% subset(group=="NHK_Mb_scramble16uL"|group=="NHK_Mb_siRNA16uL") %>% subset(min<21), 20)+
  715. ggtitle("Scramble RNA / siRNA, 16 uL")+ theme(plot.title = element_text(hjust=0.5, vjust=-1, face="bold"))
  716. ```
  718. ### [**5. Cilastatin**]{.underline}
  720. #### **Figure3B. Mg uptake with cilastatin**
  722. #################################
  724. ```{r cil, warning=F, message=F, fig.height=3.5, fig.width=4}
  725. CilDF<-read.xlsx("Data_for_analysis.xlsx", sheet = "Cil") %>% subset(ID!="comment") %>% subset(`FITC-Mgb`==0|`FITC-Mgb`==5)
  726. CilDF$group_pub <- ifelse(CilDF$group=="Mb5_Cil0", "Vehicle",
  727. ifelse(CilDF$group=="Mb5_Cil2", "Cilastatin\n2 mg/mL",
  728. ifelse(CilDF$group=="Mb5_Cil20", "Cilastatin\n20 mg/mL",
  729. ifelse(CilDF$group=="Mb5_Cil200", "Cilastatin\n200 mg/mL", "Mb0"))))
  730. CilDF$group_pub <- factor(CilDF$group_pub, levels = c("Vehicle", "Cilastatin\n2 mg/mL", "Cilastatin\n20 mg/mL", "Cilastatin\n200 mg/mL", "Mb0"))
  732. CilDF$std_value <- ifelse(CilDF$Cil==0, CilDF %>% subset(group=="Mb0_Cil0") %>% .$value %>% mean(),
  733. ifelse(CilDF$Cil==2, CilDF %>% subset(group=="Mb0_Cil2") %>% .$value %>% mean(),
  734. ifelse(CilDF$Cil==20, CilDF %>% subset(group=="Mb0_Cil20") %>% .$value %>% mean(),
  735. ifelse(CilDF$Cil==200, CilDF %>% subset(group=="Mb0_Cil200") %>% .$value %>% mean(), NA))))
  737. CilDF$adj_value <- CilDF$value/CilDF$std_value
  739. ggplot(CilDF %>% subset(`FITC-Mgb`==5), aes(group_pub, adj_value))+
  740. geom_boxplot()+
  741. geom_quasirandom(aes(shape=group_pub), position=position_jitter(height = 0), size=3, width=0.15)+
  742. scale_shape_manual(values=c(16, 15, 15, 15))+
  743. coord_cartesian(ylim=c(0.90, 1.2))+
  744. scale_y_continuous(breaks = seq(0.90, 1.2, by = 0.05))+
  745. labs(x="", y="AFU relative to control")+
  746. theme_classic2()+
  747. theme(axis.line = element_line(color = "black", size=0.8),
  748. axis.text = element_text(color = "black", face="bold", size=10),
  749. axis.title = element_text(color = "black", face="bold"),
  750. legend.position = "none", legend.title = element_blank(), legend.text = element_text(size=12, face="bold"))+
  751. annotate("text", x=3.8, y=1.1, label="ANOVA\nP=0.003", size=3.5)
  752. ```
  754. #################################
  756. #### **Fig3B. stats**
  758. #################################
  760. ```{r, cil Dunnet, meassage=F}
  761. #ANOVA and Dunnet
  762. library(DescTools) #install.packages("DescTools")
  763. DunnettTest(x= subset(CilDF, `FITC-Mgb`==5) %>% .$adj_value, g=subset(CilDF, `FITC-Mgb`==5) %>% .$group)
  764. ```
  766. ```{r, cil ANOVA, include=F, message=F}
  767. #ANOVA
  768. model<- aov(adj_value ~ group, data = CilDF %>% subset(`FITC-Mgb`==5))
  769. model %>% summary()
  770. TukeyHSD(model)
  771. ```
  773. ### [**6. VCAM1**]{.underline}
  775. #### **Fig4C. VCAM1 western blot**
  777. ```{r VCAM1WB, warning=F, message=F, fig.height=4, fig.width=4}
  778. WBDF<-read.xlsx("Data_for_analysis.xlsx", sheet = "VCAM1WB") %>% subset(ID!="comment" & group!="PC1" & group!="PC2")
  779. WBDF$group_pub <- ifelse(WBDF$group=="hNSKC_p0", "bddKC\np0",
  780. ifelse(WBDF$group=="hNSKC_p1", "bddKC\np1",
  781. ifelse(WBDF$group=="hPTEC", "bddPTC",
  782. ifelse(WBDF$group=="HK2", "HK2", "others"))))
  783. WBDF$group_pub <- factor(WBDF$group_pub, levels=c("bddKC\np0", "bddKC\np1", "bddPTC", "HK2"))
  785. ggplot(WBDF, aes(group_pub, adjv))+
  786. geom_boxplot()+
  787. geom_quasirandom(aes(shape=group_pub), position=position_jitter(height = 0), size=3, width=0.15)+
  788. #scale_color_manual(values = c("darkred","red4", "red", "blue"))+
  789. scale_shape_manual(values=c(18, 18, 16, 17))+
  790. coord_cartesian(ylim=c(0, 1.5))+
  791. #scale_y_continuous(breaks = seq(0.90, 1.15, by = 0.05))+
  792. labs(x="", y="Protein abundance (fold change)")+
  793. theme_classic2()+
  794. theme(axis.line = element_line(color = "black", size=0.8),
  795. axis.text = element_text(color = "black", face="bold", size=12),
  796. axis.title = element_text(color = "black", face="bold"),
  797. legend.position = "none", legend.title = element_blank(), legend.text = element_text(size=12, face="bold"))+
  798. annotate("text", x=2, y=0.5, label="ANOVA\nP<0.001", size=3.5)
  799. ```
  801. #################################
  803. #### **Fig4C. stats**
  805. #################################
  807. ```{r, VCAM1 ANOVA, fig.height=4, fig.width=5}
  808. #mean(SD)
  809. WBDF %>% group_by(group) %>% summarize(mean=mean(adjv), sd=sd(adjv))
  811. #ANOVA and Tukey
  812. model<- aov(adjv ~ group, data = WBDF)
  813. model %>% summary()
  814. TukeyHSD(model)
  815. ```
  817. ####################################################################
  819. ### [**7. VCAM1/LRP2 mRNA expression from ESBL**]{.underline}
  821. ####################################################################
  823. ```{r, VCAM1 and LRP2 from database, fig.height=4, fig.width=8}
  824. VC1_DATABASE <- read.csv("ESBL_data.csv") %>% filter(Gene.name=="vcam1"|Gene.name=="lrp2") %>% t() %>% as.data.frame() %>% subset(V2!="vcam1") %>% rename("VCAM1"=V2, "LRP2"=V1)
  825. VC1_DATABASE$VCAM1 <- VC1_DATABASE$VCAM1 %>% as.numeric()
  826. VC1_DATABASE$LRP2 <- VC1_DATABASE$LRP2 %>% as.numeric()
  827. VC1_DATABASE$cell <- str_sub(rownames(VC1_DATABASE), end = -4)
  829. mouse_ref <- c("mCTAL", "mCCD", "mPTS2", "Kidney", "Primary_Cell")
  830. human_line <- c("HKC8", "HKC11", "HPTC.05.LTR", "HPTC.05.CLA", "HK2")
  831. mouse_line <- c("C57BL.6J")
  832. rat_line <- c("SHR", "WKY", "NRK.E52")
  833. opossum_line <- c("OKWT", "OKH", "OK.ATCC")
  834. pig_line <- c("LLC.PK1")
  835. dog_line <- c("MDCK")
  836. VC1_DATABASE$animal <- ifelse(VC1_DATABASE$cell %in% human_line, "Human",
  837. ifelse(VC1_DATABASE$cell %in% mouse_line, "Mouse",
  838. ifelse(VC1_DATABASE$cell %in% mouse_ref, "Mouse_ref",
  839. ifelse(VC1_DATABASE$cell %in% rat_line, "Rat",
  840. ifelse(VC1_DATABASE$cell %in% opossum_line, "Opposum",
  841. ifelse(VC1_DATABASE$cell %in% pig_line, "Pig",
  842. ifelse(VC1_DATABASE$cell %in% dog_line, "Dog", NA)))))))
  845. VC1_DATABASE$animal <- factor(VC1_DATABASE$animal, levels = c("Mouse_ref", "Human", "Mouse", "Rat", "Opposum", "Pig", "Dog"))
  846. VC1_DATABASE$cell <- factor(VC1_DATABASE$cell, levels = VC1_DATABASE %>% arrange(animal, cell) %>% .$cell %>% unique())
  847. ```
  848. ```{r, VCAM1 and LRP2 from database fig, include=F, fig.height=4, fig.width=8}
  849. #VCAM1 expression in any types of cells
  850. REFAVE <- VC1_DATABASE %>% subset(animal=="Mouse_ref") %>% .$VCAM1 %>% mean()
  851. ggplot(VC1_DATABASE %>% subset(cell!="MDCK"),
  852. aes(cell, VCAM1/REFAVE, color=animal)) +
  853. geom_quasirandom() + geom_boxplot()+ theme_classic2()+
  854. geom_hline(yintercept=0, linetype="dashed")+
  855. theme(axis.text.x = element_text(angle = 45, hjust = 1), axis.title.x = element_blank())+
  856. scale_color_manual(values=c("black","red","darkgreen","blue","yellow4","purple4","blue"))+ggtitle("VCAM1")
  858. #LRP2 expression in any types of cells
  859. REFAVE2 <- VC1_DATABASE %>% subset(animal=="Mouse_ref") %>% .$LRP2 %>% mean()
  860. ggplot(VC1_DATABASE %>% subset(cell!="MDCK"),
  861. aes(cell, LRP2/REFAVE2, color=animal)) +
  862. geom_quasirandom() + geom_boxplot()+ theme_classic2()+
  863. geom_hline(yintercept=0, linetype="dashed")+
  864. theme(axis.text.x = element_text(angle = 45, hjust = 1), axis.title.x = element_blank())+
  865. scale_color_manual(values=c("black","red","darkgreen","blue","yellow4","purple4","blue"))+ggtitle("LRP2")
  867. ```
  869. ########################################
  871. #### **Supplementary figure S2&S4 \<- LRP2 and VCAM1 mRNA expression**
  873. ########################################
  875. ```{r, VCAM1 from database2, warning=F, fig.height=4, fig.width=5}
  876. VCAM1_DATABASE_Hs <-VC1_DATABASE %>% subset(animal=="Human")
  878. #VCAM1 expression in human PT cell lines
  879. REFAVE <- VCAM1_DATABASE_Hs %>% .$VCAM1 %>% mean()
  880. VCAM1_DATABASE_Hs$VCAM1_fold <- VCAM1_DATABASE_Hs$VCAM1/REFAVE
  881. ggplot(VCAM1_DATABASE_Hs,
  882. aes(cell, VCAM1_fold, color=animal)) + theme_classic2()+
  883. geom_boxplot()+
  884. geom_quasirandom(position=position_jitter(height = 0), size=3, width=0.15, aes(shape=cell)) +
  885. geom_hline(yintercept=1, linetype="dashed")+
  886. theme(axis.line = element_line(color = "black", size=0.8),
  887. axis.text.x = element_text(angle = 45, hjust = 1, size=12, face="bold"),
  888. axis.text.y = element_text(size=12, face="bold"),
  889. axis.title.y = element_text(size=12, face="bold"),
  890. axis.title.x = element_blank(),
  891. legend.position = "none",
  892. plot.title = element_text(vjust=2.5, hjust=0.5, size=14, face="bold.italic"))+
  893. scale_shape_manual(values=c(17, 16, 16, 16, 16))+
  894. scale_color_manual(values=c("black","red","darkgreen","blue","yellow4","purple4","blue"))+
  895. labs(y="mRNA expresion (fold change)", title = "VCAM1")+ annotate("text", x=4.8, y=2.5, label="ANOVA\nP=0.04", size=3.5)
  897. #LRP2 expression in human PT cell lines
  898. REFAVE2 <- VCAM1_DATABASE_Hs %>% .$LRP2 %>% mean()
  899. VCAM1_DATABASE_Hs$LRP2_fold <- VCAM1_DATABASE_Hs$LRP2/REFAVE2
  900. ggplot(VCAM1_DATABASE_Hs,
  901. aes(cell, LRP2_fold, color=animal)) + theme_classic2()+
  902. geom_boxplot()+
  903. geom_quasirandom(position=position_jitter(height = 0), size=3, width=0.15, aes(shape=cell)) +
  904. geom_hline(yintercept=1, linetype="dashed")+
  905. theme(axis.line = element_line(color = "black", size=0.8),
  906. axis.text.x = element_text(angle = 45, hjust = 1, size=12, face="bold"),
  907. axis.text.y = element_text(size=12, face="bold"),
  908. axis.title.y = element_text(size=12, face="bold"),
  909. axis.title.x = element_blank(),
  910. legend.position = "none",
  911. plot.title = element_text(vjust=2.5, hjust=0.5, size=14, face="bold.italic"))+
  912. scale_shape_manual(values=c(17, 16, 16, 16, 16))+
  913. scale_color_manual(values=c("black","red","darkgreen","blue","yellow4","purple4","blue"))+
  914. labs(y="mRNA expresion (fold change)", title = "LRP2")+ annotate("text", x=3.8, y=2.5, label="ANOVA\nP=0.87", size=3.5)
  915. ```
  917. ################################################
  919. #### **Supplementary figure S2&S4 stats.**
  921. ################################################
  923. ```{r, h_VCAM1 ANOVA, fig.height=4, include=T, fig.width=5}
  924. library(DescTools)
  925. #Summary of VCAM1/LRP2 results (mean+SD)
  926. VCAM1_DATABASE_Hs %>% group_by(cell) %>% summarise(VCAM1=paste0(round(mean(VCAM1_fold), digits=4)," (" , round(sd(VCAM1_fold), digits=4), ")"),
  927. LRP2=paste0(round(mean(LRP2_fold), digits=4)," (" , round(sd(LRP2_fold), digits=4), ")") )
  929. #p value with ANOVA (VCAM1)
  930. model<- aov(VCAM1_fold ~ cell, data = VCAM1_DATABASE_Hs)
  931. model %>% broom::tidy() %>% .[1, "p.value"]
  933. #post hoc analysis (VCAM1)
  934. DunnettTest(x= VCAM1_DATABASE_Hs %>% .$VCAM1_fold, g=VCAM1_DATABASE_Hs %>% .$cell)
  936. #p value with ANOVA (LRP2)
  937. model2<- aov(LRP2_fold ~ cell, data = VCAM1_DATABASE_Hs)
  938. model2 %>% broom::tidy() %>% .[1, "p.value"]
  940. #post hoc analysis (LRP2)
  941. #DunnettTest(x= VCAM1_DATABASE_Hs %>% .$LRP2_fold, g=VCAM1_DATABASE_Hs %>% .$cell)
  942. ```

bddPTC_analysis_PR_20260522.Rmd at commit 412ec10, under CC0-1.0 · at the source

Overview

Authors: Daiki Aomura1, Kevin G Burfeind1, Yoshio Funahashi1,2,3, Tahnee Groat1, Adam C Munhall1, Darren J Malinoski4,5, Michael P Hutchens1,5,6
ORCID iDs: Daiki Aomura, Michael P Hutchens
  1. Department of Anesthesiology and Perioperative Medicine, Oregon Health and Science University, Portland, Oregon, USA
  2. Department of Emergency and Critical Care, Nagoya University Hospital, Nagoya, Aichi, Japan
  3. Department of Nephrology, Nagoya University Hospital, Nagoya, Aichi, Japan
  4. Department of Surgery, Oregon Health and Science University, Portland, Oregon, USA
  5. Division of Operative Care, Portland VA Medical Center, Portland, Oregon, USA
  6. Division of Nephrology and Hypertension, Oregon Health and Science University, Portland, Oregon, USA
Institutions: Oregon Health & Science University (United States); Nagoya University Hospital (Japan); Portland VA Medical Center (United States)
Journal: Physiological reports, volume 14, issue 11, article e70963
Dates: received 19 April 2026; accepted 26 May 2026; published online 4 June 2026; in print June 2026
Type: Methods article · Language: English
License: CC BY
Identifiers: DOI 10.14814/phy2.70963 · PMID 42237698 · PMCID PMC13581086 · OpenAlex W7163506287
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Statistics
Keywords: endocytosis, megalin, primary kidney culture, proximal tubule, vascular cell adhesion protein 1
MeSH: Brain Death*, Epithelial Cells*, Kidney Tubules, Proximal*, Aquaporin 1, Cells, Cultured, Humans, Low Density Lipoprotein Receptor-Related Protein-2, Tissue Donors (* major topic)
Topic: Organ Donation and Transplantation (Public Health, Environmental and Occupational Health, Medicine), according to OpenAlex
Funding: U.S. Department of Veterans Affairs (I01BX04288); BLRD VA (I01 BX004288)
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Kidney proximal tubules (PT) utilize endocytosis to maintain body homeostasis. Effective PT repair mechanisms after acute kidney injury (AKI) are crucial to avoid the transition to chronic kidney disease (CKD). Cultured PT cells are often utilized to study these processes, but studies demonstrate loss of functional transporter expression in nearly all available cell lines. Here, we describe acquisition and culture of human PT cells isolated from the kidneys of brain‐dead donors (bddPTC) not suitable for transplantation and designated to research. The kidney cortex was digested, and the cells filtered through strainers were cultured. After passage 1, the cells were labeled for CD10 and CD13, and bddPTC were isolated via fluorescence‐activated sorting. bddPTC strongly expressed the brush border transport receptor megalin and aquaporin‐1 in bddPTC. Functionally, uptake of the megalin ligand myoglobin was greater in bddPTC than HK2, a human PT cell line. Pretreatment with the megalin inhibitor cilastatin, megalin‐targeted small interfering RNA, and endocytosis inhibitors reduced myoglobin uptake in bddPTC. Vascular cell adhesion protein 1, a marker for failed repair of PT after injury, was highly expressed in bddPTC. We provide reproducible means by which bddPTC may be used to study functional assessment and perhaps failed repair of PT.

Reproduced under the paper's license (CC BY), from the paper cited above.

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HutchensLab/Aomura_Burfeind_isolation_characterization

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Data availability statement

All code, raw data, and original full length blotting images used to perform analyses in this manuscript are available on the Hutchens lab GitHub (https://github.com/HutchensLab/Aomura_Burfeind_isolation_characterization).

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Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 8 MeSH terms, 2 funders, 55 references.

Cite

This paper

Aomura, D., Burfeind, K. G., Funahashi, Y., Groat, T., Munhall, A. C., Malinoski, D. J., & Hutchens, M. P. (2026). Isolation and functional characterization of primary human proximal tubular epithelial cells from brain-dead organ donors. Physiological reports, 14(11), e70963. https://doi.org/10.14814/phy2.70963

BibTeX

@article{aomura2026isolation,
author = {Aomura, Daiki and Burfeind, Kevin G and Funahashi, Yoshio and Groat, Tahnee and Munhall, Adam C and Malinoski, Darren J and Hutchens, Michael P},
title = {{Isolation and functional characterization of primary human proximal tubular epithelial cells from brain-dead organ donors}},
journal = {Physiological reports},
year = {2026},
month = jun,
volume = {14},
number = {11},
pages = {e70963},
publisher = {Wiley},
issn = {2051-817X},
doi = {10.14814/phy2.70963},
url = {https://doi.org/10.14814/phy2.70963},
pmid = {42237698},
pmcid = {PMC13581086}
}

RIS

TY - JOUR
AU - Aomura, Daiki
AU - Burfeind, Kevin G
AU - Funahashi, Yoshio
AU - Groat, Tahnee
AU - Munhall, Adam C
AU - Malinoski, Darren J
AU - Hutchens, Michael P
TI - Isolation and functional characterization of primary human proximal tubular epithelial cells from brain-dead organ donors
T2 - Physiological reports
J2 - Physiol Rep
PY - 2026
DA - 2026/06/01
VL - 14
IS - 11
SP - e70963
SN - 2051-817X
PB - Wiley
DO - 10.14814/phy2.70963
UR - https://doi.org/10.14814/phy2.70963
LA - en
ER -

CSL-JSON

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"given": "Daiki"
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"PMID": "42237698",
"PMCID": "PMC13581086",
"ISSN": "2051-817X",
"publisher": "Wiley",
"URL": "https://doi.org/10.14814/phy2.70963",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

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