diff --git a/jupyter-book/preprocessing_visualization/quality_control.ipynb b/jupyter-book/preprocessing_visualization/quality_control.ipynb
index 3681ded0..ab1bf730 100644
--- a/jupyter-book/preprocessing_visualization/quality_control.ipynb
+++ b/jupyter-book/preprocessing_visualization/quality_control.ipynb
@@ -178,9 +178,9 @@
    "id": "cb995c17-145c-4afa-bf4f-86d0842d478c",
    "metadata": {},
    "source": [
-    "## Filtering low quality reads\n",
+    "## Filtering low quality cells\n",
     "\n",
-    "The first step in quality control is to remove low-quality reads from the dataset. When a cell has a low number of detected genes, a low count depth and a high fraction of mitochondrial counts it might have a broken membrane which can indicate a dying cell. As these cells are usually not the main target of our analysis and might distort our downstream analysis, we are removing them during quality control. For identifying them, we define cell quality control (QC) threshold. Cell QC is typically performed on the following three QC covariates:\n",
+    "The first step in quality control is to remove low-quality cells from the dataset. When a cell has a low number of detected genes, a low count depth and a high fraction of mitochondrial counts it might have a broken membrane which can indicate a dying cell. As these cells are usually not the main target of our analysis and might distort our downstream analysis, we are removing them during quality control. For identifying them, we define cell quality control (QC) threshold. Cell QC is typically performed on the following three QC covariates:\n",
     "\n",
     "1. The number of counts per barcode (count depth)\n",
     "2. The number of genes per barcode\n",
@@ -315,7 +315,7 @@
    "id": "8ab11c43-396e-43b4-b247-c2d9e70b3ab6",
    "metadata": {},
    "source": [
-    "The plots indicate that some reads have a relatively high percentage of mitochondrial counts which are often associated with cell degradation. But since number of counts per cell is sufficiently high and percentage of mitochondrial reads is for most cells below 20 % we can still process the data. Based on these plots, one could now also define manual thresholds for filtering cells. Instead, we will show QC with automatic thresholding and filtering based on MAD.\n",
+    "The plots indicate that some cells have a relatively high percentage of mitochondrial counts which are often associated with cell degradation. But since number of counts per cell is sufficiently high and percentage of mitochondrial reads is for most cells below 20 % we can still process the data. Based on these plots, one could now also define manual thresholds for filtering cells. Instead, we will show QC with automatic thresholding and filtering based on MAD.\n",
     "\n",
     "First, we define a function that takes a `metric`, i.e. a column in `.obs` and the number of MADs (`nmad`) that is still permissive within the filtering strategy. "
    ]
@@ -937,7 +937,8 @@
     "\n",
     "### Reviewers\n",
     "\n",
-    "* Lukas Heumos\n"
+    "* Lukas Heumos\n",
+    "* Lukas Zappia\n"
    ]
   }
  ],