diff --git a/R/preprocessing.R b/R/preprocessing.R
index 159889081..ee35cb086 100644
--- a/R/preprocessing.R
+++ b/R/preprocessing.R
@@ -642,6 +642,139 @@ Load10X_Spatial <- function (
 
   return(object)
 }
+#' Add 10X Cell Types to a Seurat Object
+#'
+#' This function reads cell type annotations from a CSV file and adds them to the metadata of a Seurat object.
+#' If the cell type file does not exist, the original Seurat object is returned unchanged.
+#'
+#' @param data.dir A string specifying the directory containing the "cell_types" folder with the "cell_types.csv" file.
+#' @param object A Seurat object to which the cell type annotations will be added.
+#'
+#' @return A Seurat object with updated metadata including cell type annotations if the file is found.
+#'
+#' @details
+#' The function searches for a CSV file named "cell_types.csv" in the "cell_types" subdirectory within `data.dir`.
+#' The CSV file should contain at least a "barcode" column that matches the cell barcodes in the Seurat object.
+#' Additional columns in the CSV file will be merged into the Seurat object's metadata.
+#'
+#' @importFrom utils read.csv
+#' @importFrom tibble rownames_to_column column_to_rownames
+#'
+#' @examples
+#' \dontrun{
+#' # Specify the data directory containing the "cell_types" folder
+#' data.dir <- "/path/to/data"
+#'
+#' # Create a Seurat object (example)
+#' seurat_obj <- CreateSeuratObject(counts = some_counts_matrix)
+#'
+#' # Add cell type annotations to the Seurat object
+#' seurat_obj <- Add_10X_CellTypes(data.dir, seurat_obj)
+#' }
+
+Add_10X_CellTypes <- function(data.dir, object) {
+  cell_types_path <- file.path(data.dir, "cell_types", "cell_types.csv")
+  if (file.exists(cell_types_path)) {
+    cell.types <- read.csv(cell_types_path)
+    meta_data_with_barcodes <- tibble::rownames_to_column(object@meta.data, "barcode")
+    merged_meta_data <- merge(
+      x = meta_data_with_barcodes,
+      y = cell.types,
+      by = "barcode",
+      all.x = TRUE
+    )
+    object@meta.data <- tibble::column_to_rownames(merged_meta_data, "barcode")
+    return(object)
+  } else {
+    return(object)
+  }
+}
+
+#' Load a 10x Genomics Single Cell Experiment into a \code{Seurat} object
+#'
+#' @inheritParams Read10X
+#' @inheritParams SeuratObject::CreateSeuratObject
+#' @note If multiome 10x data the assay param will not be used. The names of each assay contained in the matrix are used.
+#' @param data.dir Directory containing the H5 file specified by \code{filename}
+#' @param filename Name of H5 file containing the feature barcode matrix
+#' @param to.upper Converts all feature names to upper case. This can provide an
+#' approximate conversion of mouse to human gene names which can be useful in an
+#' explorative analysis. For cross-species comparisons, orthologous genes should
+#' be identified across species and used instead.
+#' @param ... Arguments passed to \code{\link{Read10X_h5}}
+#'
+#' @return A \code{Seurat} object
+#'
+#'
+#' @export
+#' @concept preprocessing
+#'
+#' @examples
+#' \dontrun{
+#' data_dir <- 'path/to/data/directory'
+#' list.files(data_dir) # Should show filtered_feature_bc_matrix.h5
+#' Load10X(data.dir = data_dir)
+#' }
+#'
+Load10X <- function(data.dir, filename = "filtered_feature_bc_matrix.h5",
+                    assay = "RNA", to.upper = FALSE, ...) {
+
+  if (length(data.dir) > 1) {
+    stop("`data.dir` expects a single directory path but received multiple values.")
+  }
+  if (!file.exists(data.dir)) {
+    stop("No such file or directory: '", data.dir, "'")
+  }
+
+
+  filename <- list.files(data.dir, filename, full.names = FALSE, recursive = FALSE)
+  counts.path <- file.path(data.dir, filename)
+  if (!file.exists(counts.path)) {
+    stop("File not found: '", counts.path, "'")
+  }
+
+  counts <- Read10X_h5(counts.path, ...)
+
+  if (is.list(counts)) {
+    counts <- lapply(counts, function(mat) {
+      rownames(mat) <- toupper(rownames(mat))
+      mat
+    })
+  } else {
+    rownames(counts) <- toupper(rownames(counts))
+  }
+
+  if (is.list(counts)) {
+    seurat.list <- lapply(names(counts), function(name) {
+      CreateSeuratObject(
+        counts = counts[[name]],
+        assay = name,
+        project = name
+      )
+    })
+
+    for (i in seq_along(seurat.list)) {
+      if (Assays(seurat.list[[i]]) %in% c("Gene Expression", "RNA")) {
+        seurat.list[[i]] <- Add_10X_CellTypes(data.dir, seurat.list[[i]])
+      }
+    }
+
+    merged.object <- merge(
+      x = seurat.list[[1]],
+      y = seurat.list[-1],
+      add.cell.ids = names(counts),
+      merge.data = FALSE
+    )
+    return(merged.object)
+
+  } else {
+    object <- CreateSeuratObject(counts, assay = assay)
+    if (Assays(object)  %in%  c("Gene Expression", "RNA")) {
+      object <- Add_10X_CellTypes(data.dir, object)
+    }
+    return(object)
+  }
+}
 
 
 #' Read10x Probe Metadata
@@ -1263,8 +1396,8 @@ Read10X_Image <- function(
       image = image
     )
 
-    # As of v5.1.0 `Radius.VisiumV1` no longer returns the value of the 
-    # `spot.radius` slot and instead calculates the value on the fly, but we 
+    # As of v5.1.0 `Radius.VisiumV1` no longer returns the value of the
+    # `spot.radius` slot and instead calculates the value on the fly, but we
     # can populate the static slot in case it's depended on.
     visium.v1@spot.radius <- Radius(visium.v1)
 
@@ -3520,7 +3653,7 @@ SampleUMI <- function(
 #' replaces the \code{NormalizeData} → \code{FindVariableFeatures} →
 #' \code{ScaleData} workflow by fitting a regularized negative binomial model
 #' per gene and returning:
-#' 
+#'
 #' - A new assay (default name “SCT”), in which:
 #'   - \code{counts}: depth‐corrected UMI counts (as if each cell had uniform
 #'     sequencing depth; controlled by \code{do.correct.umi}).
@@ -3531,13 +3664,13 @@ SampleUMI <- function(
 #'
 #' When multiple \code{counts} layers exist (e.g. after \code{split()}),
 #' each layer is modeled independently. A consensus variable‐feature set is
-#' then defined by ranking features by how often they’re called “variable” 
+#' then defined by ranking features by how often they’re called “variable”
 #' across different layers (ties broken by median rank).
-#' 
+#'
 #' By default, \code{sctransform::vst} will drop features expressed in fewer
 #' than five cells. In the multi-layer case, this can lead to consenus
 #' variable-features being excluded from the output's \code{scale.data} when
-#' a feature is "variable" across many layers but sparsely expressed in at 
+#' a feature is "variable" across many layers but sparsely expressed in at
 #' least one.
 #'
 #' @param object A Seurat object or UMI count matrix.
@@ -3593,11 +3726,11 @@ SampleUMI <- function(
 #' @seealso \code{\link[sctransform]{vst}},
 #'   \code{\link[sctransform]{get_residuals}},
 #'   \code{\link[sctransform]{correct_counts}}
-#' 
+#'
 #' @rdname SCTransform
 #' @concept preprocessing
 #' @export
-#' 
+#'
 SCTransform.default <- function(
   object,
   cell.attr,
@@ -4557,7 +4690,7 @@ FindSpatiallyVariableFeatures.Seurat <- function(
     verbose = verbose,
     ...
   )
-  
+
   object <- LogSeuratCommand(object)
 
   return(object)