diff --git a/paper/paper.bib b/paper/paper.bib
index bb8f4dc..b60893f 100644
--- a/paper/paper.bib
+++ b/paper/paper.bib
@@ -125,7 +125,7 @@ @Article{Hutchison2024
 }
 
 @Manual{Schloerke2025,
-title  = {GGally: Extension to 'ggplot2'},
+title  = {GGally: Extension to ggplot2},
 author = {Barret Schloerke and Di Cook and Joseph Larmarange and Francois Briatte and Moritz Marbach and Edwin Thoen and Amos Elberg and Jason Crowley},
 year = {2025},
 note = {R package version 2.4.0},
@@ -134,7 +134,7 @@ @Manual{Schloerke2025
 }
 
 @Manual{Briatte2026,
-title = {{ggnetwork}: Geometries to Plot Networks with 'ggplot2'},
+title = {{ggnetwork}: Geometries to Plot Networks with ggplot2},
 author = {François Briatte},
 year = {2026},
 note = {R package version 0.5.15},
@@ -144,7 +144,7 @@ @Manual{Briatte2026
 
 @Article{Hao2024,
 author = {Hao, Yuhan and Stuart, Tim and Kowalski, Madeline H and Choudhary, Saket and Hoffman, Paul and Hartman, Austin and Srivastava, Avi and Molla, Gesmira and Madad, Shaista and Fernandez-Granda, Carlos and Satija, Rahul},
-title = {Dictionary learning for integrative, multimodal and scalable single-cell analysis.},
+title = {Dictionary learning for integrative, multimodal and scalable single-cell analysis},
 journal = {Nature Biotechnology},
 year = {2024},
 volume = {42},
@@ -155,7 +155,7 @@ @Article{Hao2024
 
 @Article{Righelli2022,
 author = {Righelli, Dario and Weber, Lukas M and Crowell, Helena L and Pardo, Brenda and Collado-Torres, Leonardo and Ghazanfar, Shila and Lun, Aaron T L and Hicks, Stephanie C and Risso, Davide},
-title = {SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor},
+title = {SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in {R} using {B}ioconductor},
 journal = {Bioinformatics},
 volume = {38},
 number = {11},
diff --git a/paper/paper.md b/paper/paper.md
index 7ee394f..b0d1c0d 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -41,7 +41,7 @@ affiliations:
 
 # Summary
 
-A graph describes a network structure of nodes connected by edges, and is used across many areas of science to represent relationships between entities. When the relationships themselves are spatial, node positions carry physical meaning, and the graph must be further represented within an external frame of reference. In R, *igraph* [@Nepusz2006] provides powerful tools for network analysis, and the *ggplot2* ecosystem [@Wickham2016] for visualization, drawing nodes and edges as separate layers. Integrating the two remains challenging, however, because the geometry of these layers is not fully coordinated at rendering time, a consequence of *ggplot2*'s input model, in which each plot layer consumes a single rectangular table and is resolved independently. This design works remarkably well for most applications but is restrictive for graph structures with mutually dependent components. Because layers do not share transformed states, the graph is not rendered as a single coherent object. Here we introduce *RGraphSpace*, a streamlined interface that integrates *igraph* with *ggplot2* graphics within a normalized coordinate space. It synchronizes node and edge layers, jointly resolving their geometry under standard aesthetic mappings. This coherence lets the graph be anchored to external reference frames, where it becomes a spatial object embedded in a wider context.
+A graph describes a network structure of nodes connected by edges, and is used across many areas of science to represent relationships between entities. When the relationships themselves are spatial, node positions carry physical meaning, and the graph must be further represented within an external frame of reference. In R, *igraph* [@Nepusz2006] provides powerful tools for network analysis, and the *ggplot2* ecosystem [@Wickham2016] does the same for visualization, drawing nodes and edges as separate layers. Integrating the two remains challenging, however, because the geometry of these layers is not fully coordinated at rendering time, a consequence of *ggplot2*'s input model in which each plot layer consumes a single rectangular table and is resolved independently. This design works remarkably well for most applications but is restrictive for graph structures with mutually dependent components. Because layers do not share transformed states, the graph is not rendered as a single coherent object. Here we introduce *RGraphSpace*, a streamlined interface that integrates *igraph* with *ggplot2* graphics within a normalized coordinate space. It synchronizes node and edge layers, jointly resolving their geometry under standard aesthetic mappings. This coherence lets the graph be anchored to external reference frames, where it becomes a spatial object embedded in a wider context.
 
 # Statement of need