diff --git a/R/Trajectory_CellTypes_v12.R b/R/Trajectory_CellTypes_v12.R
new file mode 100644
index 0000000..6cc59ab
--- /dev/null
+++ b/R/Trajectory_CellTypes_v12.R
@@ -0,0 +1,327 @@
+#' Trajectory analysis with TSCAN
+#'
+#' Perform the CCBR single-cell RNA-seq trajectory workflow using TSCAN.
+#'
+#' @param Seurat_Object A Seurat object or path to an RDS file containing one.
+#' @param MetaData A data.frame with per-cell metadata; must contain the clustering column.
+#' @param Clusters_to_Use Character scalar naming the metadata column to use for clusters.
+#' @param Custom_Gene_List Optional character vector or delimited string of genes to highlight.
+#'
+#' @return Invisibly returns `NULL`. Generates trajectory and pseudotime plots as side effects.
+#' @examples
+#' \donttest{
+#' if (requireNamespace("Seurat", quietly = TRUE)) {
+#'   seurat_example <- Seurat::CreateSeuratObject(
+#'     counts = matrix(rpois(200, lambda = 5), nrow = 20,
+#'                     dimnames = list(paste0("g", 1:20), paste0("c", 1:10)))
+#'   )
+#'   seurat_example <- Seurat::AddMetaData(
+#'     object = seurat_example,
+#'     metadata = data.frame(cluster = sample(letters[1:2], 10, replace = TRUE),
+#'                           row.names = colnames(seurat_example))
+#'   )
+#'   Trajectory_CellTypes(
+#'     Seurat_Object = seurat_example,
+#'     MetaData = Seurat::FetchData(seurat_example, vars = c("cluster")),
+#'     Clusters_to_Use = "cluster"
+#'   )
+#' }
+#' }
+#' @export
+Trajectory_CellTypes <- function(Seurat_Object,
+                                 MetaData,
+                                 Clusters_to_Use,
+                                 Custom_Gene_List = NULL) {
+  required_packages <- c(
+    "Seurat", "scater", "scran", "scuttle", "TrajectoryUtils", "TSCAN",
+    "ggplot2", "gridExtra", "mclust",
+    "SingleCellExperiment", "SummarizedExperiment"
+  )
+  ensure_namespace(required_packages)
+
+  seurat_object <- load_seurat_object(Seurat_Object)
+
+  if (!is.data.frame(MetaData)) {
+    stop("MetaData must be a data.frame.", call. = FALSE)
+  }
+  if (!is.character(Clusters_to_Use) || length(Clusters_to_Use) != 1L ||
+      is.na(Clusters_to_Use)) {
+    stop("Clusters_to_Use must be a non-missing character scalar.", call. = FALSE)
+  }
+  if (!Clusters_to_Use %in% colnames(MetaData)) {
+    stop(sprintf("Column '%s' not found in MetaData.", Clusters_to_Use), call. = FALSE)
+  }
+
+  cache_original <- Sys.getenv("R_USER_CACHE_DIR", unset = NA_character_)
+  on.exit({
+    if (is.na(cache_original)) {
+      Sys.unsetenv("R_USER_CACHE_DIR")
+    } else {
+      Sys.setenv(R_USER_CACHE_DIR = cache_original)
+    }
+  }, add = TRUE)
+  cache_dir <- tools::R_user_dir("Trajectory_CellTypes", which = "cache")
+  dir.create(cache_dir, recursive = TRUE, showWarnings = FALSE)
+  Sys.setenv(R_USER_CACHE_DIR = cache_dir)
+
+  if (!is.null(rownames(MetaData)) && all(colnames(seurat_object) %in% rownames(MetaData))) {
+    MetaData <- MetaData[colnames(seurat_object), , drop = FALSE]
+  }
+
+  user_clusters <- MetaData[[Clusters_to_Use]]
+  if (length(user_clusters) != ncol(seurat_object)) {
+    warning("MetaData rows do not match Seurat object cells; attempting to align by cell names.")
+    shared_cells <- intersect(colnames(seurat_object), rownames(MetaData))
+    if (length(shared_cells) == 0) {
+      stop("Unable to align MetaData with Seurat object cells.", call. = FALSE)
+    }
+    MetaData <- MetaData[shared_cells, , drop = FALSE]
+    MetaData <- MetaData[colnames(seurat_object), , drop = FALSE]
+    user_clusters <- MetaData[[Clusters_to_Use]]
+  }
+  if (length(user_clusters) != ncol(seurat_object)) {
+    stop("MetaData does not align with the Seurat object cells after attempted matching.", call. = FALSE)
+  }
+
+  label_column <- resolve_cluster_column(seurat_object, MetaData, Clusters_to_Use)
+  if (!label_column %in% colnames(seurat_object@meta.data)) {
+    seurat_object[[label_column]] <- user_clusters
+  }
+  cluster_labels <- seurat_object[[label_column, drop = TRUE]]
+  Seurat::Idents(seurat_object) <- cluster_labels
+
+  custom_gene_list <- normalise_gene_list(Custom_Gene_List)
+
+  message("The following genes were used:")
+  gene_universe <- rownames(seurat_object)
+  genes_present <- intersect(custom_gene_list, gene_universe)
+  if (length(genes_present) == 0) {
+    message("None")
+  } else {
+    message(paste(genes_present, collapse = ", "))
+  }
+
+  message("The following genes were not found:")
+  missing_genes <- setdiff(custom_gene_list, gene_universe)
+  if (length(missing_genes) == 0) {
+    message("None")
+  } else {
+    message(paste(missing_genes, collapse = ", "))
+  }
+
+  custom_gene_list <- genes_present
+
+  sce <- Seurat::as.SingleCellExperiment(seurat_object)
+  SummarizedExperiment::colData(sce)$label <- cluster_labels
+
+  aggregated <- scuttle::aggregateAcrossCells(sce, ids = cluster_labels)
+  centroids <- SingleCellExperiment::reducedDim(aggregated, "PCA")
+  mst <- TSCAN::createClusterMST(centroids, clusters = NULL)
+
+  line_data_tsne <- TSCAN::reportEdges(aggregated, mst = mst, clusters = NULL, use.dimred = "TSNE")
+  line_data_tsne <- harmonise_edge_axes(line_data_tsne, "tSNE")
+  plot_tsne1 <- scater::plotTSNE(sce, colour_by = "label") +
+    ggplot2::geom_line(data = line_data_tsne, ggplot2::aes(x = tSNE_1, y = tSNE_2, group = edge))
+
+  line_data_umap <- TSCAN::reportEdges(aggregated, mst = mst, clusters = NULL, use.dimred = "UMAP")
+  line_data_umap <- harmonise_edge_axes(line_data_umap, "UMAP")
+  plot_umap1 <- scater::plotUMAP(sce, colour_by = "label") +
+    ggplot2::geom_line(data = line_data_umap, ggplot2::aes(x = UMAP_1, y = UMAP_2, group = edge))
+
+  mapping <- TSCAN::mapCellsToEdges(sce, mst = mst, use.dimred = "PCA")
+  ordered <- TSCAN::orderCells(mapping, mst)
+  common_pseudo <- TrajectoryUtils::averagePseudotime(ordered)
+
+  plot_tsne2 <- scater::plotTSNE(sce, colour_by = I(common_pseudo), text_by = "label", text_colour = "red") +
+    ggplot2::geom_line(data = line_data_tsne, ggplot2::aes(x = tSNE_1, y = tSNE_2, group = edge)) +
+    ggplot2::ggtitle("TSCAN-based MST")
+
+  plot_umap2 <- scater::plotUMAP(sce, colour_by = I(common_pseudo), text_by = "label", text_colour = "red") +
+    ggplot2::geom_line(data = line_data_umap, ggplot2::aes(x = UMAP_1, y = UMAP_2, group = edge)) +
+    ggplot2::ggtitle("TSCAN-based MST")
+
+  pseudo_all <- TSCAN::quickPseudotime(sce, use.dimred = "PCA")
+  pseudo_mnn <- TSCAN::quickPseudotime(sce, use.dimred = "PCA", dist.method = "mnn")
+  if (!is.null(pseudo_mnn$connected$TSNE)) {
+    pseudo_mnn$connected$TSNE <- harmonise_edge_axes(pseudo_mnn$connected$TSNE, "tSNE")
+  }
+  if (!is.null(pseudo_mnn$connected$UMAP)) {
+    pseudo_mnn$connected$UMAP <- harmonise_edge_axes(pseudo_mnn$connected$UMAP, "UMAP")
+  }
+  mnn_pseudo <- TrajectoryUtils::averagePseudotime(pseudo_mnn$ordering)
+
+  plot_tsne3 <- scater::plotTSNE(sce, colour_by = I(mnn_pseudo), text_by = "label", text_colour = "red") +
+    ggplot2::geom_line(data = pseudo_mnn$connected$TSNE, ggplot2::aes(x = tSNE_1, y = tSNE_2, group = edge)) +
+    ggplot2::ggtitle("TSCAN with MNN distances-based MST")
+
+  plot_umap3 <- scater::plotUMAP(sce, colour_by = I(mnn_pseudo), text_by = "label", text_colour = "red") +
+    ggplot2::geom_line(data = pseudo_mnn$connected$UMAP, ggplot2::aes(x = UMAP_1, y = UMAP_2, group = edge)) +
+    ggplot2::ggtitle("TSCAN with MNN distances-based MST")
+
+  sce$Pseudotime <- TrajectoryUtils::pathStat(ordered)[, 1]
+  pseudo_tests <- TSCAN::testPseudotime(sce, pseudotime = ordered[, 1])[[1]]
+  sorted_tests <- pseudo_tests[order(pseudo_tests$p.value), ]
+
+  downregulated <- sorted_tests[sorted_tests$logFC < 0, , drop = FALSE]
+  downregulated$SYMBOL <- rownames(downregulated)
+  top_down <- head(downregulated$SYMBOL, 10)
+  plot_decrease <- scater::plotExpression(
+    sce,
+    features = top_down,
+    x = "Pseudotime",
+    colour_by = "label",
+    ncol = 5
+  ) + ggplot2::ggtitle("Expression of the top 10 genes that decrease with pseudotime")
+
+  upregulated <- sorted_tests[sorted_tests$logFC > 0, , drop = FALSE]
+  upregulated$SYMBOL <- rownames(upregulated)
+  top_up <- head(upregulated$SYMBOL, 10)
+  plot_increase <- scater::plotExpression(
+    sce,
+    features = top_up,
+    x = "Pseudotime",
+    colour_by = "label",
+    ncol = 5
+  ) + ggplot2::ggtitle("Expression of the top 10 genes that increase with pseudotime")
+
+  on_first_path <- !is.na(sce$Pseudotime)
+
+  print(Seurat::DimPlot(seurat_object))
+  set.seed(1)
+  plot(pseudo_all$mst)
+  print(plot_tsne1)
+  gridExtra::grid.arrange(plot_tsne2, plot_tsne3, ncol = 2)
+  print(plot_umap1)
+  gridExtra::grid.arrange(plot_umap2, plot_umap3, ncol = 2)
+  plot(plot_decrease)
+  plot(plot_increase)
+  scater::plotHeatmap(
+    sce[, on_first_path],
+    order_columns_by = "Pseudotime",
+    colour_columns_by = "label",
+    features = head(top_up, 50),
+    center = TRUE,
+    main = "Expression of the top 50 genes that increase with pseudotime"
+  )
+  scater::plotHeatmap(
+    sce[, on_first_path],
+    order_columns_by = "Pseudotime",
+    colour_columns_by = "label",
+    features = head(top_down, 50),
+    center = TRUE,
+    main = "Expression of the top 50 genes that decrease with pseudotime"
+  )
+
+  if (length(custom_gene_list) > 0) {
+    custom_scatter <- scater::plotExpression(
+      sce,
+      features = custom_gene_list,
+      x = "Pseudotime",
+      colour_by = "label",
+      ncol = 5
+    ) + ggplot2::ggtitle("Expression of custom genes across pseudotime")
+    plot(custom_scatter)
+
+    scater::plotHeatmap(
+      sce[, on_first_path],
+      order_columns_by = "Pseudotime",
+      colour_columns_by = "label",
+      features = custom_gene_list,
+      center = TRUE,
+      main = "Expression of custom genes across pseudotime"
+    )
+  }
+
+  invisible(NULL)
+}
+
+utils::globalVariables(c(
+  "label", "edge", "Pseudotime", "SYMBOL", "UMAP_1", "UMAP_2",
+  "tSNE_1", "tSNE_2", "mst"
+))
+
+ensure_namespace <- function(pkgs) {
+  missing <- vapply(pkgs, function(pkg) !requireNamespace(pkg, quietly = TRUE), logical(1))
+  if (any(missing)) {
+    stop(
+      sprintf(
+        "Missing required packages: %s",
+        paste(pkgs[missing], collapse = ", ")
+      ),
+      call. = FALSE
+    )
+  }
+  invisible(TRUE)
+}
+
+load_seurat_object <- function(input) {
+  if (inherits(input, "Seurat")) {
+    return(input)
+  }
+  if (is.character(input) && length(input) == 1L && file.exists(input)) {
+    return(readRDS(input))
+  }
+  if (requireNamespace("nidapFunctions", quietly = TRUE)) {
+    path <- tryCatch(
+      nidapFunctions::nidapGetPath(input, "seurat_object.rds"),
+      error = function(e) NULL
+    )
+    if (!is.null(path) && file.exists(path)) {
+      return(readRDS(path))
+    }
+  }
+  stop(
+    "Provide a Seurat object or a path to an RDS file containing one.",
+    call. = FALSE
+  )
+}
+
+resolve_cluster_column <- function(seurat_object, metadata, selected) {
+  seurat_columns <- colnames(seurat_object@meta.data)
+  if (selected %in% seurat_columns) {
+    return(selected)
+  }
+  idx <- match(selected, colnames(metadata))
+  if (!is.na(idx) && idx <= length(seurat_columns)) {
+    candidate <- seurat_columns[idx]
+    if (!is.na(candidate) && nzchar(candidate)) {
+      return(candidate)
+    }
+  }
+  normalised_selected <- make.names(selected)
+  matches <- seurat_columns[make.names(seurat_columns) == normalised_selected]
+  if (length(matches) == 1) {
+    return(matches)
+  }
+  selected
+}
+
+normalise_gene_list <- function(genes) {
+  if (is.null(genes) || length(genes) == 0) {
+    return(character())
+  }
+  if (length(genes) == 1L) {
+    genes <- unlist(strsplit(gsub(",", " ", genes), " "))
+  }
+  genes <- genes[genes != ""]
+  unique(trimws(genes))
+}
+
+harmonise_edge_axes <- function(edge_df, prefix) {
+  if (is.null(edge_df) || nrow(edge_df) == 0) {
+    return(edge_df)
+  }
+  targets <- c(sprintf("%s_1", prefix), sprintf("%s_2", prefix))
+  lower_targets <- tolower(targets)
+  current_names <- names(edge_df)
+  for (i in seq_along(targets)) {
+    required <- targets[i]
+    if (!required %in% current_names) {
+      candidates <- current_names[tolower(current_names) == lower_targets[i]]
+      if (length(candidates) == 1L) {
+        names(edge_df)[names(edge_df) == candidates] <- required
+      }
+    }
+  }
+  edge_df
+}
diff --git a/tests/testthat/test-Trajectory_CellTypes_v12.R b/tests/testthat/test-Trajectory_CellTypes_v12.R
new file mode 100644
index 0000000..e7afd4d
--- /dev/null
+++ b/tests/testthat/test-Trajectory_CellTypes_v12.R
@@ -0,0 +1,97 @@
+skip_if_packages_missing <- function(pkgs) {
+  for (pkg in pkgs) {
+    testthat::skip_if_not_installed(pkg)
+  }
+}
+
+source(testthat::test_path("..", "..", "R", "Trajectory_CellTypes_v12.R"))
+
+build_test_seurat <- function() {
+  set.seed(42)
+  counts <- matrix(
+    rpois(200, lambda = 5),
+    nrow = 20,
+    dimnames = list(paste0("g", seq_len(20)), paste0("c", seq_len(10)))
+  )
+  seurat_obj <- Seurat::CreateSeuratObject(counts = counts)
+  seurat_obj <- Seurat::NormalizeData(seurat_obj, verbose = FALSE)
+  seurat_obj <- suppressWarnings(
+    Seurat::FindVariableFeatures(
+      seurat_obj,
+      selection.method = "vst",
+      nfeatures = 10,
+      verbose = FALSE
+    )
+  )
+  seurat_obj <- Seurat::ScaleData(seurat_obj, features = rownames(seurat_obj), verbose = FALSE)
+  seurat_obj <- Seurat::RunPCA(seurat_obj, npcs = 5, verbose = FALSE)
+  seurat_obj <- Seurat::RunTSNE(
+    seurat_obj,
+    dims = 1:5,
+    perplexity = 2,
+    check_duplicates = FALSE,
+    verbose = FALSE
+  )
+  seurat_obj <- Seurat::RunUMAP(
+    seurat_obj,
+    dims = 1:5,
+    n.neighbors = 5,
+    verbose = FALSE
+  )
+  clusters <- sample(letters[1:2], ncol(seurat_obj), replace = TRUE)
+  names(clusters) <- colnames(seurat_obj)
+  Seurat::AddMetaData(
+    object = seurat_obj,
+    metadata = data.frame(cluster = clusters, row.names = names(clusters))
+  )
+}
+
+required_pkgs <- c(
+  "Seurat", "scater", "scran", "scuttle", "TrajectoryUtils", "TSCAN",
+  "ggplot2", "gridExtra", "mclust",
+  "SingleCellExperiment", "SummarizedExperiment"
+)
+
+
+ testthat::test_that("Trajectory_CellTypes errors when clustering column missing", {
+  skip_if_packages_missing(required_pkgs)
+  seurat_obj <- build_test_seurat()
+  metadata <- data.frame(other = rep("a", ncol(seurat_obj)), row.names = colnames(seurat_obj))
+  testthat::expect_error(
+    Trajectory_CellTypes(
+      Seurat_Object = seurat_obj,
+      MetaData = metadata,
+      Clusters_to_Use = "cluster"
+    ),
+    "Column 'cluster' not found"
+  )
+})
+
+ testthat::test_that("Trajectory_CellTypes validates argument types", {
+  skip_if_packages_missing(required_pkgs)
+  seurat_obj <- build_test_seurat()
+  metadata <- data.frame(cluster = seurat_obj$cluster, row.names = colnames(seurat_obj))
+  testthat::expect_error(
+    Trajectory_CellTypes(
+      Seurat_Object = seurat_obj,
+      MetaData = metadata,
+      Clusters_to_Use = 123
+    ),
+    "non-missing character scalar"
+  )
+})
+
+ testthat::test_that("Trajectory_CellTypes runs on minimal input", {
+  skip_if_packages_missing(required_pkgs)
+  seurat_obj <- build_test_seurat()
+  metadata <- data.frame(cluster = seurat_obj$cluster, row.names = colnames(seurat_obj))
+  set.seed(7)
+  testthat::expect_invisible(
+    Trajectory_CellTypes(
+      Seurat_Object = seurat_obj,
+      MetaData = metadata,
+      Clusters_to_Use = "cluster",
+      Custom_Gene_List = "g1,g2"
+    )
+  )
+})